Book reviews and abstracts

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Book reviews and abstractsThaddeus Shannon aa Portland State UniversityPublished online: 24 Sep 2010.

To cite this article: Thaddeus Shannon (2003) Book reviews and abstracts, International Journal of General Systems, 32:1, 89-102, DOI:10.1080/0308107021000035442

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BOOK REVIEWS AND ABSTRACTS

BOOK REVIEWS

FACETS OF SYSTEMS SCIENCE (Second Edition), by George J. Klir. Kluwer.

Academic/Plenum Publishers, New York, 2001, XVII1740 pages, ISBN 0-306-46623-6.

What is systems science? Anyone associated with the field has been asked this question many

times. Systems science is the study of systems, but there are significant facets of systems that

are not the concern of systems science. The distinction between systems properties and

properties of systems, is key to properly describing systems science.

The book under review is Klir’s answer to this question. This second edition is not so much

updated as it is improved. The book is still divided into two parts. The first contains Klir’s

view of what systems science is, the second is a collection of classic essays from the systems

literature. All chapters in the first part of the book are now divided into subsections and all the

chapters containing technical material now include a selection of exercises. The division into

subsections makes it easier to find material, particularly in chapters two, six and seven. While

relatively few in number, the exercises are well chosen for the material covered and help to

clarify the presentation. Both these changes make the text more useful both for self-study and

as a class text. Additionally, a number of figures have been added that clarify particular

conceptual issues under discussion.

The author expands upon three themes throughout part one: systems science as the study

of knowledge structures, methods for systems study and the study of methods and the Janus

of complexity with its opposing paths of simplicity and certainty. The systems science theme

begins with the constructive definition of a system as a set of objects with a relation. The

basic ramifications of this now standard definition are explored throughout the significantly

revised second chapter. This chapter has been substantially expanded with the inclusion of a

well-illustrated introduction to sets, relations, their associated mathematics and the notion of

isomorphism. Together these additions should help non-mathematical readers better

understand the definition of a general system.

The object and relation view of systems is then fleshed out through the historical

discussion of the third chapter. The fourth chapter introduces Klir’s systems taxonomy

based on his epistemological hierarchy. This material forms the central structure for

defining systems science and sets up the definitive statement of the seventh chapter: that

systems knowledge is knowledge about knowledge structures. While not explicitly

stated, this implies that systems science is the pursuit of knowledge about knowledge

structures.

The theme of methodology is taken up in chapter five and the study of methodologies

is considered in chapter six as systems metamethodology. Metamethodology as the study of

ISSN 0308-1079 print/ISSN 1563-5104 online q 2003 Taylor & Francis Ltd

DOI: 10.1080/0308107021000035442

International Journal of General Systems, 2003 Vol. 32 (1), pp. 89–102

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methods is part and parcel of the study of knowledge structures, since methods are operators

on knowledge structures. This theme is continued on a more mundane level in chapter seven

in a discussion of the roles of computing devices in systems science. This discussion, while

motivated by historical developments, is limited to a finite state machine perspective.

Traditional analog computation appears in a separate discussion of systems models and

contemporary soft computing techniques are hardly mentioned.

Though issues of system simplification run through many chapters, the theme of

complexity explicitly arises in chapter eight. The treatment begins by pointing out that the

complexity of a system can only be talked about in a specific context. This view is in

opposition to that of many contemporary complex systems researchers, who come from

specific disciplines other than systems science and who would like to exhibit complexity

as a universal property. Three kinds of complexity are introduced: complexity as the

amount of information needed to describe a system, complexity as the amount of

information needed to resolve uncertainties related to the system in a given context and

computational complexity. An overview of measures of uncertainty—nonspecificity,

conflict (dissonance) and fuzziness (vagueness) and the trade off between system

description length and uncertainty based complexity in the modeling context is included.

The ninth chapter focuses on the problems and methods of simplification. New in the

second edition is a brief introduction to reconstructability analysis as a simplification

methodology.

The collection of articles that form the second part of the book has been revised with

one article omitted and three added. The omitted article, by Klir himself, tied the

emergence of systems science as an intellectual pursuit to the shift from an industrial to a

post-industrial or information society. This central idea is now included in the last section

of chapter three.

The new paper by Havel delves into the scale dimensions, both temporal and spatial that

describe perceptual experience of the world. The distribution of objects over scales is

considered and fields of relevance or scale spectrums are used to illustrate the depth or

thinness of significant phenomena. A newly included paper by Shaw and Gaines on eliciting

personal and societal constructs for system and problem definition dovetails with Klir’s

epistemological framework. It points out possible validity criteria for each epistemological

level and discusses some of the difficulties and techniques for appropriately defining a source

system for a particular context. A recent brief paper by Zadeh introduces the motivation and

mechanics for using fuzzy logic in modeling, system identification and control. The paper is

a very quick survey of fuzzy sets, inference, control rules and the relationship of fuzzy

systems to other soft computing methods.

While the first edition could be used as a textbook, its layout was more appropriate for use

in a seminar. The second edition is ideal for use in a one-semester introductory systems

science class at the graduate or undergraduate level. Anyone working in the fields of complex

systems, artificial life, applied dynamical systems and the like should be interested in this

book because it describes the process and limitations associated with abstracting knowledge

and techniques from the study of artificial systems, for the purpose of understanding or

constructing real systems. In summary, this second edition is a well polished refinement of

the original. I heartily recommend this book to everyone who has ever wondered, “What is

systems science?”

THADDEUS SHANNON

Portland State University

BOOK REVIEWS AND ABSTRACTS90

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EFFECTIVE REQUIREMENTS PRACTICES, by Ralph R. Young. Addison-Wesley,

Boston, 2001, 400 pages, ISBN 0-201-70912-0.

Introduction

Projects fail. Research indicates that organizations do not spend enough time and money on the

requirements process. The Standish Group (1995) states that over one third of information

technology projects have major problems directly related to requirements gathering,

requirements management and requirements documentation. This is not trivial because

problems related to poor requirements compound like bacteria in a festering wound. The

literature clearly indicates that when requirements errors are not caught in the early stages of a

project, they are particularly painful and expensive to fix (Leffingwell and Widrig, 2000).

Despite the empirical evidence, many organizations do not properly emphasize the

requirements process. Research by Davis (1993) indicates that repairing defects at the

requirements phase of a project is dramatically more cost effective than repairing defects

during design, coding, testing, or after deployment. It is hard to believe that project

managers, engineers and developers would ignore this. It is much more probable that people

simply lack access to the information, or their eyes and ears are turned in other directions.

Some books have done an excellent job getting people to think about why projects fail,

such as The Mythical Man-Month (1995) by Fred Brooks. Brooks mainly focuses on the

human side of project failure. Peopleware: Productive Projects and Teams (Demarco and

Lister, 1999) takes a similar approach to explaining project success and failure. While both of

these books are excellent, they do not offer deep insight into project requirements.

Other books, such as Exploring Requirements: Quality Before Design (Gause and

Weinberg, 1989), dive into project requirements. Gause and Weinberg make it easy to

understand why requirements are important. Furthermore, they offer several simple tools and

quirky but useful examples. While their view on requirements might be a bit idiosyncratic,

the book is required reading if you are interested in requirements.

Taken as a whole, the requirements engineering literature is interesting but lacks cohesion.

Many books focus on the entire project development process (e.g. Brooks, 1995), while

others focus on requirements for certain types of products or projects. For example,

Managing Software Requirements (Leffingwell and Widrig, 2000) is a good book, but its

focus is on software.

Pulling it Together

Effective Requirements Practices by Ralph R. Young is the book that you need if you want to

understand the core ideas of requirements engineering. It brings just the right level of focus to

the requirements process while remaining practical for both researchers and industry

practitioners. Perhaps more importantly, it is the glue that binds together the requirements

literature.

Young’s book is clean and easy to read. The language is crisp and reading each chapter

only takes a few minutes. This does not mean that the book lacks meaning. Indeed, the reason

the book packs such a punch is that each chapter contains what it needs and only what it

needs. Content is well organized into charts and graphs and there are plenty of checklists and

bulleted lists. If you want to read it quickly, you can. On the other hand, at the end of each

chapter there are several annotated references. There is plenty of information to digest and

there are many paths for you to explore. This is a key feature of the book; it makes the book

BOOK REVIEWS AND ABSTRACTS 91

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worth owning. If the chapter references are not sufficient enough reason to own this book,

then consider that it also has a good index, good glossary and even a useful list of acronyms.

It would not be fair to characterize Effective Requirements Practices as a perfect book.

Like any human endeavor, there are some flaws. First, the book lacks color and pizzazz.

The figures and diagrams feel trapped in black and white; the line drawings are plain. Since

they are so ordinary, they are not very memorable which is unfortunate because so much of

the information is excellent. It would have been wise to spice this book up with some

attention getting material, if it is meant to capture the attention of project managers.

Second, the supporting media channels are not useful. For example, on the last page of the

book there is a web site listed. If you enter the web site address into your web browser, you

will be dished up a Page Not Found error. Ouch! Also, the CD included with the book does

not seem to include anything juicy. It is simply the same information found in the book, but

on a CD. This is obviously a problem for people looking for more information. It puts a black

mark on the book that it does not otherwise deserve.

Third, the book seems to focus on large systems projects versus smaller (perhaps more

typical) projects. This is probably the result of Young having dealt with large scale projects

during his career. He is the Director of Software Engineering, Systems and Process

Engineering, at Litton PRC, Inc., a provider of information technology and systems-based

solutions. On the other hand, maybe it is because academic researchers have focused on

requirements engineering for large, complicated systems.

Finally, like other books on this topic, it lacks focus on the end users. There are several

references to developers, managers, designers and customers, but there is very little focus on

the actual people who will be using the tools and products being built. It always seems that

end users are neglected. That just doesn’t make any sense.

Final Thoughts

The next time you hear about a project failing, remember that it probably failed because

of poor requirements. Most designers and developers are able to build what they are

tasked to build. Those folks are clever. The problem is that most requirements do not

capture what people actually want. The bottom line is that projects do fail, but they

don’t have to fail. It is not fate; it is usually just poor requirements. You have more

control than you think. When the right people have the right knowledge and apply it at

the right time, projects will succeed.

Ralph Young’s Effective Requirements Practices is a reference manual and a best practicer

guide. The early chapters give you the background you need, while the middle chapters are

full of useful methods and tools. For example, Chapter 7 will tell you exactly, how to

maintain and augment project communication, while Chapter 10 will give you tactics for

dealing with scope creep and requirements changes. The final chapter of the book gives you

suggestions on what you can do with all of your new knowledge. It is short and sweet, but it

packs a powerful punch. In summary, if you want to understand requirements and you want

your projects to succeed, then Effective Requirements Practices is the book you need. Don’t

miss it.

References

Brooks, F.P. (1995) The Mythical Man-Month (Addison-Wesley, Reading, Mass.).Davis, A. (1993) Software Requirements: Objects, Functions and States (Prentice Hall, Englewood Cliffs, NJ).Demarco, T. and Lister, T.R. (1999) Peopleware: Productive Projects and Teams, 2nd Ed. (Dorset House, New York).


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Gause, D.C. and Weinberg, G.R. (1989) Exploring Requirements: Quality Before Design (Dorset House, New York).Leffingwell, D. and Widrig, D. (2000) Managing Software Requirements: A Unified Approach (Addison-Wesley,

Reading, Mass.).The Standish Group (1995) Charting the Seas of Technology: The CHAOS Study (The Standish Group International,

Dennis, Mass.).JOHN S. RHODES

E-mail: [email protected]://webword.com

PLAUSIBLE NEURAL NETWORKS FOR BIOLOGICAL MODELLING, edited by

Henk A.K. Mastebroek and Johan E. Vos. Kluwer Academic Publishers, 2001, IX1259

pages, ISBN 0-7923-7192-5.

Plausible Neural Networks for Biological Modelling is an edited volume reviewing

the developments of artificial neural networks for biological modeling. As mentioned by

the editors, the volume is intended for “. . .advanced students, postgraduates and scientist

in the field of neuroscience who want to get acquainted with the possibilities for

studying the nervous system by modeling. . .”. Nonetheless, at times the volume appears

to be more appropriate for engineers and neuroscience researchers who are already

familiar with the standard operation of artificial neural networks but are interested in

developing more realistic biologically-inspired architectures. Neuroscience students

unfamiliar with the field may at first fail to see the connection between real neuro-

modeling and the popular field of artificial neural networks, which has certainly taken

liberties with its “inspiration”.

The volume is broken down into two parts: Fundamentals and Applications to Biology. Part 1

on fundamentals begins with an overview of the evidence for synapse modification,

beginning with Hebbian learning and following through with an explanation of the

neurotransmitters and the synaptic modification process by way of long term potentiation

(LTP) and depression (LTD). Although the material at times feels a little dated and

shortchanges the comparison between real and artificial neural network learning, it does

provide a good introduction to the important properties of biological learning. The

presentation here may also be more useful to non-neuroscientists.

The second chapter explores the difference between spiking and single action potential

neurons. This is an important distinction given that most artificial neural networks assume

a single on/off action potential, while many biological neurons communicate using pulses.

The chapter does a good job of explaining spike and rate coding, the use of the “integrate

and fire” model and the necessity of using spike coding for modeling fast transients,

synchrony and coincidence detection. The impact of spike coding on learning is also

explored.

The third chapter overviews various recurrent neural network models. An understanding of

these architectures is essential for anyone designing plausible neural networks for biological

modeling due to the tremendous amount of lateral and feedback neural connections present

in the brain. Although a detailed comparison of recurrent neural network learning rules with

the popular backpropagation learning algorithm would have helped to reinforce the

biological plausibility of these networks, the chapter does go on to give an excellent overview

of the time dynamics and various types of recurrent neural network architectures. As an

extension, the fourth chapter presents a detailed derivation of the learning rules for dynamic

recurrent neural networks and will be useful for both real and artificial neural network

modelers.

The applications in Part 2 of the volume contains seven chapters that describe various

neural network models of sensory and motor control tasks, each designed to illustrate the


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requirements for biological plausibility outlined in the first part of the volume. This begins

with a simulation of the human oculomotor integrator, used to take incoming velocity signals

and transform them into position signals. Here, a recurrent neural network is used to take

advantage of the time delays in signal processing, essential for the visual integration. The

presentation will be interesting to both engineers and neuroscientist due to its potential use

and biologically plausible explanation. A chapter on pattern segmentation in an associative

network of spiking neurons will also be attractive to engineers and neuroscientist for similar

reasons. The implications of solving the binding problem are profound for everything from

artificial vision to understanding the nature of consciousness. A chapter on line and edge

detection by curvature-adaptive neural networks begins with a description of the Difference

of Gaussian and Gabor filters, discusses their benefits for line and edge detection and their

correlation to retina and visual cortex processes and then presents the biological plausibility

of the algorithms. Once again, this chapter will be attractive to both engineering and

neuroscientists.

Three chapters on movement and motor control are included to further highlight the

benefits of recurrent connectivity for biological modeling. This begins with a description of

the neuro-anatomy that should be explored when modeling voluntary motor control. Cellular

neuron firing patterns in cortical areas 4 and 5 of the monkey, links between neurons and

sensory systems, effects of time delay and a detailed description of the VITE model for

providing an integrative approach for cortical control of voluntary movement are discussed.

The non-equation block diagram presentation is beneficial for illustrating and allowing for an

appreciation of the connectivity and interactions between the various neural structures

necessary for motor control.

A chapter on the implications of activity dependent processes in the spinal cord

circuits for the development of motor control provides a neural network model for

explaining muscle control. Highlights include how recurrent connections, Hebbian

learning and rhythmic activity dynamics can cause activity dependent changes in the

connections of the spinal circuitry. Descriptions of the ability of the network to control

joint angle and stiffness are also presented by way of experimentation. Another chapter

explores how lateral connections can be used to build topological representations that

are useful for solving problems of motor control that utilize sensorimotor information, in

particular, those that involve speech. This chapter is excellent for illustrating the

progression from theory, hypothesis, modeling and experimentation. It is also useful for

showing the benefits of such a topographical mapping for doing more than just

explaining and generating receptive field properties. An additional chapter on path

planning and obstacle avoidance using recurrent neural networks has also been included

and will be valuable for engineers interested in this area, although its biological

underpinnings are not as well described as in the other chapters involving movement and

motor control.

All together this edited volume should prove useful to neuroscience students with a

previous initial introduction to artificial neural networks who are looking for ways to use this

tool to do their own biologically modeling. The applications included in the second part of

the volume will provide inspiration by illustrating how modeling might be achieved in their

own areas of research, especially when it involves taking advantage of lateral connections

and recurrent networks. Finally, the presentation of material will also prove valuable to

engineers who either have a curiosity in the original inspiration for artificial neural networks,

or who are looking for new biologically-inspired approaches to improve their current

network architectures.

DAVID ENKE

University of Missouri—Rolla


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NEURO-FUZZY CONTROL OF INDUSTRIAL SYSTEMS WITH ACTUATOR

NONLINEARITIES, by F. L. Lewis, J. Campos and R. Selmic. Society for Industrial and

Applied Mathematics (SIAM), Philadelphia, 2002, XIV1244 pages, ISBN 0-89871-505-9.

Contrary to most book on neuro-fuzzy control, control techniques based on neural networks

and fuzzy systems are not viewed in this book as alternatives to classical control techniques,

but rather as significant enhancements of the latter. This view, which is reflected in the content

of the book, is expressed exceedingly well by the authors themselves in the following quote

from Preface to the book:

Modern control techniques were developed using frequency domain, state-space and nonlinear systemstechniques that were responsible for very effective flight control systems, space system controllers, ship andsubmarine controllers, vehicle engine control systems and industrial manufacturing and process controllers.Recently the increasing complexity of manmade systems has placed severe strains on these modern controllerdesign techniques. More stringent performance requirements in both speed of response and accuracy havechallenged the limits of modern control. Different operation regimes require controllers to be adaptive andhave switching and learning capabilities. Tolerance to faults and failures requires controllers to have aspects ofintelligent systems. Complex systems have unknown disturbances, unmodeled dynamics and unstructureduncertainties. The actuators that drive modern systems can be hydraulic, electrical, pneumatic and so on andhave severe nonlinearities in terms of friction, deadzones, backlash, or time delays. . . . In this book, we exploreimproved controller design through two sorts of intelligent controllers, those based on neural networks andthose based on fuzzy logic systems. Neural networks capture the parallel processing and learning capabilitiesof biological nervous systems and fuzzy logic captures the decision-making capabilities of human linguisticand cognitive systems.

This book thus brings together in a purposeful way classical control systems with neural

networks and fuzzy systems to achieve more powerful control capabilities. The reader is

provided with relevant background in these three areas in Chapters 1 and 2. The use of neural

networks and fuzzy systems for enhancing control of nonlinear systems of three types is then

examined in the next three chapters. Chapter 3 deals with control of systems with friction

(a complicated nonlinear phenomenon in which a force is produced that tends to oppose the

motion in a mechanical system); Chapter 4 focuses on systems with deadzones (a static

nonlinearity that describes the insensitivity of the system to small signals); and Chapter 5 is

oriented to systems with backlash (the difference between toothspace and tooth width in

mechanical gearing systems).

Chapter 6 is dedicated to a particular control problem, the control of vehicle active

suspension. It is shown how the universal function approximation capabilities of fuzzy systems

can greatly enhance an adaptive backstepping controller designed for this problem. Chapter 7

is concerned with the use of neural networks in feedback controllers of nonlinear systems that

are based on techniques known as “adaptive critic techniques.” Chapter 8 deals with the

relatively new area of control of telerobotic systems with time delays. It is shown how neural

networks can be utilized effectively for compensating the time delays a improving thus the

overall performance of the system. Finally, Chapter 9 deals with the various implementation

issues regarding controllers studied in previous chapters. Also included in the book are four

appendices containing computer programs (written in C) that are needed for building

controller of the types described in the book for real-time applications.

This is a sophisticated book at the frontiers of nonlinear control systems. All topics covered

in the book are treated rigorously. This includes the various stability proofs, which, in turn, are

verified by computer simulations. The book should be of great value for anyone interested in

control systems (practicing engineers in industry, university researchers, graduate students),

but it requires knowledge of at least basic ideas of classical control theory.

GEORGE J. KLIR

Binghamton University—SUNY


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DEVELOPMENT AND EVOLUTION: COMPLEXITY AND CHANGE IN

BIOLOGY, by Stanley N. Salthe. The MIT Press, Cambridge, Massachusetts, 1993,

XIV1357 pages, ISBN 0-262-19335-3.

Stanley N. Salthe’s book is an ambitious outline of a natural philosophy of general biological

systems, a Naturphilosophie that is aimed at contributing to the unity of sciences. It is

nothing less than an attempt to deconstruct and reconstruct theoretical biology guided by a

constructionist version of general systems theory, thermodynamics, information theory and

hierarchical structuralism. The underlying emphasis in the work is to propose

developmentalism, as Salthe terms it, as the viable alternative to the Darwinian framework

for understanding biological and pre-biological systems. In this work, many disparate

subjects—e.g. the history of Western scientific thought, postmodernist critiques of

realist/mechanicist philosophical presuppositions and evolutionary theory—are woven

together to form an imposing metatheory. The book builds on his 1985 work, Evolving

Hierarchical Systems (Columbia University Press) and branches out into many new areas

(e.g. specification hierarchies) with a strong repudiation of the mechanistic assumptions of

the previous work. The book is a thoroughly social constructionist work by virtue of the

stance taken on both science and philosophy. Salthe wants the reader to know that the book is

not a finished work; it is rather, in the author’s words, “sketchy and suggestive” (p. XII).

Ideas presented in the book challenge the reader to rethink his understanding of natural

systems, especially the relation between system and interpreter/modeler of the system and

biological systems in particular.

One can view Development and Evolution as an “impressionist” rendering of how

biology and a philosophy of natural systems might look minus the constraints of

philosophical realism, mechanicism and reductionism. The more practical minded student

of natural systems might feel compelled to ask, though, what warrants such a book? That

question can be answered in several ways, but the most important response, it seems, is the

need to develop a theoretical and philosophical biology in the grand tradition of, say,

Driesch or Bertalanffy. Such a task is not for the faint-hearted, especially since theoretical

and philosophical biology have been languishing from the latter half of the twentieth

century onwards due to several influences. How have these two areas of thought been

fading? First, there was the rise of molecular biology and the concomitant view that, to

understand biological systems, all that is necessary is to disassemble organisms or their

components to the level of the underlying molecules and attempt to rebuild the system in

vitro. This epistemological/ontological reductionism is the motivation behind the ongoing

genome projects and finds many adherents in the biological sciences. The impact molecular

biology has had on the theoretical and philosophical side of the study of life is largely one

of negation; thus, many eschew theory and thought in favor of “wet experiments” and data.

Second, the kind of theoretical biology that has persisted through the “molecular

revolution” can be referred to as “mathematization,” following Salthe (p. X), where rather

“simple” problems are explored via computationally tractable models. One result of

mathematization, however, is that the outstanding and difficult theoretical and

philosophical issues (such as the question, What are attractors in natural systems?) are

often ignored or dismissed, in favor of more feasible (easily published) endeavors. So the

refusal to accord theory an important role in understanding biological systems and a “drunk

under the lamppost” mathematization that often passes for theory or serious reflection (to

borrow a criticism from Ron Brady), have diminished the significance of theoretical

biology in the thoughts of many biologists. But perhaps the major factor impeding

theoretical and philosophical biology is the unwillingness on the part of many to question

the corpus of tacit assumptions. It is presupposed by many (if not most) that the study of


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biological systems has a solid foundation (neoDarwinism) and the important task at hand is

to layer data on that foundation. The latter stance is inimical to deep thinking about biology

because such reflection is seen as superfluous. In other words, if we have all the major

answers at hand, then why bother posing “esoteric” questions? Keeping these three points

in mind, then, positions one in a better position to see the real purpose of Salthe’s book and

why it is warranted. The purpose of Development and Evolution is to make the argument

that the “solid foundation” of biology—a web of tacit presuppositions—is not rock but,

rather, ideological sand; and that the study of natural systems must be rethought from the

foundation up, involving out of necessity both “deep theory” and philosophy. And the

reason why such a book is warranted is simply that it is raising very important questions

concerning systems thinking, systems philosophy and biology.

The organization of the book and the topics covered are as follows. The Introduction

(chapter 1) is a presentation and discussion of various and agreeing theoretical frameworks

upon which his general theory of transforming complex systems is based. Chapter 2 centers

on hierarchical structuralism and provides both an overview of scalar hierarchies

(emphasized in Salthe’s 1985 work) and a theoretical refinement of them, in addition to

an introduction to specification hierarchies, the latter providing a novel means for

understanding the development of natural systems. In the third chapter, the relation between

nonequilibrium thermodynamics and information theory, touched upon in Chapter 1, is made

clearer. Salthe presents infodynamics as the basis for a generalized developmental theory,

while arguing that the externalism of the Baconian/Cartesian/Newtonian/Darwinian/Com-

tean (BCNDC) version of science must be replaced by a semiotic internalism. One key

insight in Chapter 3 is the idea that developing systems senesce as too many informational

constraints are accreted and this thesis is expounded throughout the remainder of the book.

Development as self-organization is the focus of the fourth chapter; material structures such

as organisms begin as vague, immature systems, pass on into more informationally rich

mature systems and then enter a highly determinate condition that eventually is recycled.

Salthe makes a distinction between development and evolution and postulates that higher-

level scalar entities (such as lineages) can self-organize to form developing “trajectories.” In

Chapter 5, the problem of the emergence of change—change in the sense of the radically new

in space-time—in developing systems is reconsidered from the developmentalist

perspective. The last chapter is an examination of the Darwinian worldview (e.g. cosmology

and ethics) contrasted with developmentalism. The objective of Salthe in chapter 6 is to

construct a “Western, science-based creation myth” for the purpose of countering the

prevailing Darwinian system. Finally, there is an Appendix with the paper “The constructive

universe and the evolutionary systems framework”, by Juan Alvarez de Lorenzana.

The reader should note that this is not a book that is casually read and it would appear that

the author designed it that way. A single paragraph can contain several important ideas, each

competing for and worthy of serious thought. The book is thus best read slowly so that ample

time can be given to ponder what the author is saying, for making notes and for reconsidering

assumptions.

A few final and pointed remarks are in order. It is the reviewer’s opinion that the author has

done himself a disservice by cloaking his thoughts in postmodernist garb. Many of the ideas

presented in Development and Evolution will be unfamiliar to most non-theoretical and

evolutionary biologists and commingling those ideas with, e.g. notions derived from Marxist

and feminist discourse will repel many. It is one thing to ask the naıve realist to rethink his

view of systems and the world and another to ask that he adopt instead questionable

ideological stances. Given that “realism” can take many forms, to present constructionism as

the alternative to naıve realism is a bit too simplistic. Few if any systems theorists and

biologists really conflate their models with reality “out there,” indeed many appear to be


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indifferent regarding science as a means to Truth, so the strong social constructivism adopted

by Salthe in the book seems to be in opposition to a straw man. Furthermore, Salthe provides

no solid (convincing) reasons to accept the constructionist, internalist, semiotic, et cetera,

view of natural systems; rather, these “isms” appear to have the role of rhetorical devices,

supporting the critique of modern biological thought. And should the postmodernist lines of

thought used in Development and Evolution fall out of fashion, then many of the truly

significant ideas in the book may appear to outdated. The isms aside, Salthe has nevertheless

written one of the most stimulating books in the field.

RICHARD V. STERNBERG

National Center for Biotechnology Information—GenBank

National Institutes of Health

STABLE ADAPTIVE CONTROL AND ESTIMATION FOR NONLINEAR SYS-TEMS: NEURAL AND FUZZY APPROXIMATOR TECHNIQUES, by Jeffrey

T. Spooner, Manfredi Maggiore, Raul Ordonez and Kevin M. Passino. John Wiley, New

York, 2002, XVII1545 pages, ISBN 0-471-41546-4.

Within the rapidly growing number of books on neuro-fuzzy control, this book is

distinguished by three special features: (i) traditional robust control techniques for nonlinear

systems are successfully merged with neuro-fuzzy control techniques; (ii) neural and fuzzy

approximation techniques play an important role in the book; and (iii) the book is intended to

serve as a graduate text.

The book consists of fifteen chapters, fourteen of which (Chapters 2–15) are divided into

four parts. Chapter 1 plays a special role. It contains an introduction to the whole book and an

expression of philosophical views of the authors upon which the book is based. The

following is their own summary of these views (quoted from page 10):

1. We use concepts and techniques from robust control theory.

2. Adaptive approaches are used to compensate for unknown system characteristics.

3. When a system uncertainty may be characterized by a function, the problem is

reformulated in terms of fuzzy systems or neural networks to extend the applicability of

the adaptive approaches.

These three statements capture roughly the content of the book. Let me briefly describe what

actually is covered in the four parts of the book.

The role of Part I (Chapters 2–5, pp. 11–131) is to cover relevant background material.

This includes mathematical foundations regarding systems stability and optimization, an

introduction to neural networks and fuzzy systems and mathematical background pertaining

to function approximation.

Part II (Chapter 6–9, pp. 133–303) is oriented to state-feedback control of continuous-

time nonlinear dynamic systems. A great deal of attention is devoted to the questions of how

to cope with uncertainties and information deficiencies of various kinds. After examining

non-adaptive systems, it is then shown in great detail how adaptivity can be utilized to

improve both system robustness and performance.

Part III (Chapters 10–12, pp. 305–434) is concerned with output-feedback control of

continuous-time nonlinear dynamic systems. Here, the state of a system involved is not

available for feedback. Again, the main issue addressed in this part is how to handle various

uncertainties and how adaptivity can be utilized for this purpose.

Part IV (Chapters 13–15, pp. 435–509) covers extensions of the previous material to

discrete-time systems and to decentralized systems with constraints on information exchange


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between subsystems. In addition, Chapter 15 is dedicated to the comparison of methods

presented in this book with conventional methods of adaptive control as well as with various

other method pursued recently in the literature (e.g. methods based on genetic algorithms,

expert systems, planning systems, etc.).

My overall impression of the book is very favorable. The material, which is quite

advanced, is treated rigorously and, yet, it is relatively easy to comprehend. This is

undoubtedly due to many examples employed in the book for illustrating various concepts

or techniques, but also due to extraordinarily clear presentation. It is also helpful that the

first section in each chapter is an overview (or rather a preview) of that chapter, which

focuses on its purpose and the last section is a summary of what is actually covered in the

chapter. Each chapter with the exception of Chapter 1 and Chapter 15 contains also a set

of well-thought exercises and design problems. Moreover, the book contains numerous

case studies of real-world applications and an excellent annotated bibliography for further

study.

In summary, this is an excellent book. It is pedagogically sound and, hence, suitable as a

text for graduate courses. However, I recommend it also as a very valuable resource to

practitioners in the area of control systems.

GEORGE J. KLIR


RUNS AND SCANS WITH APPLICATIONS WILEY SERIES IN PROBABILITY

AND STATISTICS, by N. Balakrishnan and Markos V. Koutras. John Wiley, New York,

2002, XXII1422 pages, ISBN 0-471-24892-4.

Runs are uninterrupted sequences of objects. A scan is a statistic that is applied

sequentially on the data to detect a specified pattern of runs. More specifically, suppose we

have a sequence of binary objects, say S and F. The following is an example of a sequence

of 20 such objects: SFFSSSSFFSFFSSSFSSFS. In this sequence, we see 6 runs of S’s and

5 runs of F’s. These runs are also of different size. Among the S runs there are 3 runs of

size 1, 1 run of size 2, 1 runs of size 3 and 1 run of size 4. Similarly, the F runs ore of

different size. Suppose that we wish to detect where for the first time a run of 3 S’s start.

We can define a scan statistic, which counts the number of S’s among a sequence of 3

letters. This scan statistic is applied sequentially, starting at the first letter. It stops as soon

as the value of the scan statistic reaches the number 3. In the above example, the number

of S’s among the first three letters is 1. The statistic slides then to the second position,

where its value is 1. At the third position its value is 2 and at the fourth position it reaches

the value 3 for the first time. The first run of three S’s starts at position 4 and continues

also in position 5.

Various statistical questions can be asked about the distribution of the number of runs of

different kinds under complete random mixing of the letters (when all possible

permutations are equally probable), the distributions of the size of runs, or the distribution

of the number of runs “up” and “down”. In the above example, if F follows a letter, we can

say that there is a run “down” and if by S the run is “up”. Thus in the above sequence,

there are 8 runs down and 11 runs up. Does the difference between the numbers of runs up

and runs down signify lack of randomness? This is a very important question in non-

parametric (distribution free) tests of randomness, non-parametric tests of equality of

distributions, etc. Applications of the theory of runs are found in statistical process

monitoring and control, in sampling acceptance schemes for the demonstration of the


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quality of new products, in reliability of systems, in molecular biology, queueing systems

and many other fields.

The present book consists of 12 chapters, a bibliography of over 600 articles or books on

the subjects, an Author Index, a Subject Index, 25 tables for the use of the practitioner and

many figures. The following is a list of the 12 chapters:

1. Introduction and historical remarks

2. Waiting for the first run occurrence

3. Applications

4. Waiting for multiple run occurrences

5. Number of Run Occurrences

6. Sooner/latter run occurrences

7. Multivariate run-related distributions

8. Applications

9. Waiting for the first scan

10. Waiting for multiple scans

11. Number of scan occurrences

12. Applications

The book is organized in three parts. Each part ends with an applications chapter. The first

part deals with the distributions of waiting times (stopping times) till the occurrence of a

particular run and consists of chapters 2 and 3. The second part deals with multiple and

multivariate runs and consists of chapters 4 to 8. The third part deals with scans and consists

of the last four chapters.

To study this book, one needs to know the techniques of discrete probability, as in the

celebrated book of W. Feller, An Introduction to Probability Theory and its

Applications,Vol. I, Third Edition, John Wiley and Sons, New York, 1968. These

techniques include some combinatorial analysis, recursive equations, generating functions.

Markov chains techniques can be used very effectively to find the distributions of stopping

times relating to the number of Bernoulli trials needed until the first appearance of a run of

length k of S’s. Thus, I find that the book would be very useful to the researcher in probability

and statistics. It would be a great book to use in a one semester graduate course on special

topics. The book is clearly written and the authors seem to be very knowledgeable on the

topic. The lack of problems for solution at the end of each chapter diminishes from the value

of the book. As said earlier, it is a great resource for the researcher in non-parametric statistic.

SHELEMYAHU ZACKS

Department of Mathematical Sciences


GENERAL SYSTEMS THEORY: IDEAS AND APPLICATIONS, by Lars Skyttner.

World Scientific, Singapore, 2001, XII1459 pages, ISBN 981-02-4175-5.

As the title of the reviewed book suggests, the subject of the book coincides with the one to

which this journal is primarily oriented. The book should thus be of interest to readers of this

journal.

According to the author, the book was written as an introductory text for students in

systems science. While this is certainly the primary use of the book, its utility is much

broader. In my opinion, it is a useful source for anyone interested in systems science.


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For beginners, the book is an easy introduction to systems science; for others, it is a useful

overview, supplemented with many interesting and thought provoking personal

commentaries by the author.

The book is divided into two parts. The first part is devoted to an overview of historical

roots of systems movement and a survey of basic ideas, concepts laws and principles of

systems science. It consists of five chapters: (1) The Emergence of Holistic Thinking;

(2) Basic Ideas of General Systems Theory; (3) A Selection of Systems Theories; (4)

Communication and Information Theory and (5) Some Theories of Brain and Mind.

The second part of the book is primarily oriented to applications of systems thinking and

systems methodology in various areas of human affairs, but it also contains a discussion of

the prospective future of systems science. It consists of six chapters: (6) Artificial

Intelligence and Life; (7) Organizational Theory and Management; (8) Decision-Making and

Decision Aids; (9) Informatics; (10) Some of the Systems Methodologies and (11) The

Future of Systems Theory.

By and large, this book is welcome addition to the literature on systems science. It is well

written, but, unfortunately, far from comprehensive. Work of some important contributors

such as Rosen, Mesarovic, Wymore, Zeigler, Casti, Bellman, Zadeh, Von Foerster, Pask and

others were not even mentioned. Moreover, some important areas of systems science such as

chaotic systems, fuzzy systems, developmental systems, soft computing, reconstructability

analysis and others are completely ignored. In spite of these shortcoming, the book has many

positive features and I fully recommend it to readers of this journal.

GEORGE J. KLIR


ABSTRACTS

FUZZY RELATIONAL SYSTEMS: FOUNDATIONS AND PRINCIPLES, by Radim

Belohlavek. Kluwer Academic/Plenum Publishers, New York, 2002, XII1369 pages, ISBN

0-306-46777-1.

This book presents a general theory of fuzzy relational systems and concentrates on selected

general issues of fuzzy relational modeling in the framework of the developed theory. It

discusses phenomena hidden in the ordinary bivalent case, as well as new topics in fuzzy

relational systems, such as object-attribute fuzzy relations and fuzzy concept lattices,

similarity and fuzzy closure operators. Both mathematicians and engineers will find the

book an invaluable teaching and reference resource in modeling and fuzzy logic.

INTELLIGENT CONTROL SYSTEMS: AN INTRODUCTION WITH EXAMPLES,

by Katalin M. Hangos, Rozalia Lakner and Miklos Gerzson. Kluwer Academic Publishers,

Boston. 2000, XVI1299 pages, ISBN 1-4020-0134-7.

Intelligent control is a rapidly developing, complex and challenging field with great practical

importance and potential. Because of the rapidly developing and interdisciplinary nature of

the subject, there are only a few edited volumes consisting of research papers on intelligent

control systems but little is known and published about the fundamentals and the general

know-how in designing, implementing and operating intelligent control systems.


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Intelligent control system emerged from artificial intelligence and computer controlled

systems as an interdisciplinary field. Therefore, the book summarizes the fundamentals of

knowledge representation, reasoning, expert systems and real-time control systems and then

discusses the design, implementation, verification and operation of real-time expert systems

using G2 as an example. Special tools and techniques applied in intelligent control are also

described including qualitative modelling, Petri nets and fuzzy controllers. The material is

illlustrated with simple examples taken from the field of intelligent process control.

The book is suitable for advanced undergraduate students and graduate engineering

students. In addition, practicing engineers will find it appropriate for self-study.

FUNDAMENTALS OF MATRIX COMPUTATION (Second Edition), by David

S. Watkins. John Wiley, New York, 2002, XIII1618 pages, ISBN 0-471-21394-2.

Matrix computations lie at the heart of most scientific and engineering computational tasks.

It is thus essential for any scientist or engineer to understand how to perform matrix

computations efficiently and accurately. This book explains matrix computations and the

accompanying theory clearly and in detail, along with useful insights. Although it is written

for graduate and upper-division undergraduate courses, it is also suitable for practicing

scientists and engineers. The book contains many examples and exercises that make use of

MATLAB. Compared with the First Edition, the Second Edition is significantly expanded.

CATEGORICAL DATA ANALYSIS (Second Edition), by Alan Agresti. John Wiley, New

York, 2002, XV1710 pages, ISBN 0-471-36093-7.

The Second Edition of this classic book is substantially modified and expanded. Designed

for statisticians as well as for scientists and graduate students practicing statistics, the book

offers a comprehensive introduction to the most important methods for categorical data

analysis. Special features of the book include:

. More than 100 analysis of “real” data sets.

. More than 600 exercises at the end of individual chapters, some directed towards theory

and methods and some towards applications.

. An appendix regarding relevant computer software.

. Notes at the end of each chapter that provide references for recent research and many

topics not covered in the text.

The associated web site (www.stat.ufl.edu/~aa/cda/cda.html) offers information on the use

of other software that not covered in the text, data sets for examples and exercises, answers to

some exercises and additional exercises.


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