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Introduction to Neuro-fuzzy and Soft computing. G.Anuradha (Lecture 1). What is computing?. Counting, calculating - PowerPoint PPT Presentation
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04/20/23 1
Introduction to Neuro-fuzzy and Soft computing
G.Anuradha
(Lecture 1)
What is computing?
Counting, calculating
The discipline of computing is the systematic study of algorithmic processes that describe and transform information: their theory, analysis, design, efficiency, implementation, and application.
Types of computingHard computing
Soft Computing
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Differences between hard and soft computingHard Computing Soft computing
Precisely stated analytical model Tolerant to imprecision, uncertainty, partial truth, approximation
Based on binary logic, crisp systems, numerical analysis, crisp software
Fuzzy logic, neural nets, probabilistic reasoning.
Programs are to be written Evolve their own programs
Two values logic Multi valued logic
Exact input data Ambiguous and noisy data
Strictly sequential Parallel computations
Precise answers Approximate answers
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Essence of SC:-Accommodation with the pervasive imprecision of the real world
Principle of SC:-Exploit uncertainty to achieve robustness and better rapport with reality
Artificial intelligence
If intelligence can be induced in machines it is called as artificial intelligence.
Soft computing is a part of artificial intelligent techniques
Closed related to machine intelligence/computational intelligence
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What is Soft computing
Neural Networks
Fuzzy Inferencesystems
Neuro-Fuzzy
Computing
Derivative-Free
OptimizationSoft Computing
+ =
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Artificial Neural Networks
Evolutionary computation
Fuzzy logic
Heuristics
Soft Computing
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Introduction
SC is an innovative approach to constructing computationally intelligent systems Intelligent systems that possess humanlike expertise within a specific domain, adapt themselves and learn to perform better in changing environments These systems explain how they make decisions or take actions They are composed of two features: “adaptivity” & “knowledge
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Introduction Contd….
Neural Networks (NN) that recognize patterns & adapts themselves to cope with changing environments
Fuzzy inference systems that incorporate human knowledge & perform inference & decision making
Adaptivity + Expertise = NF & SC
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What is the difference between Fuzzy Logic and Neural Networks?
Fuzzy logic allows making definite decisions based on imprecise or ambiguous data
ANN tries to incorporate human thinking process to solve problems without mathematically modeling them.
Both these methods can be used to solve nonlinear problems, and problems that are not properly specified, but they are not related.
ANN tries to apply the thinking process in the human brain to solve problems.
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Latest developments in the field of soft computing
Areas of image processingImage retrieval
Image analysis
Remote sensing
Data miningSwarm intelligence
Diffusion process
Agent’s technology
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Swarm Technology
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SC Constituents and Conventional AI
“SC is an emerging approach to computing which parallel the remarkable ability of the human mind to reason and learn in a environment of uncertainty and imprecision” [Lotfi A. Zadeh, 1992]SC consists of several computing paradigms including:
NN Fuzzy set theoryApproximate reasoningDerivative-free optimization methods such as genetic algorithms (GA) & simulated annealing (SA)
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SC constituents (the first three items) and conventional AI
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These methodologies form the core of SC
In general, SC does not perform much symbolic manipulation
SC in this sense complements conventional AI approaches
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character recognizer
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Features of Conventional AI
From conventional AI to computational intelligence
Conventional AI manipulates symbols on the assumption that human intelligence behavior can be stored in symbolically structured knowledge bases: this is known as: “ The physical symbol system hypothesis
The knowledge-based system (or expert system) is an example of the most successful conventional AI product
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What is an expert system?
An expert system is software that uses a knowledge base of human expertise for problem solving, or to clarify uncertainties where normally one or more human experts would need to be consulted
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Expert system
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Building blocks of expert system
Knowledge base: factual knowledge and heuristic knowledgeKnowledge representation: in the form of rulesProblem solving model: forward chaining or backward chainingKnowledge base: knowledge gained by an individual user
Note:-Knowledge engineering:- building an expert systemKnowledge engineers:- practitioners.
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Applications of expert system
1. Diagnosis and Troubleshooting of Devices and Systems of All Kinds
2. Planning and Scheduling
3. Configuration of Manufactured Objects from Subassemblies
4. Financial Decision Making
5. Knowledge Publishing
6. Design and Manufacturing
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Several definitions have been given to conventional AI
“AI is the study of agents that exists in an environment and perceive and act” [S. Russel & P. Norvig] “AI is the act of making computers do smart things” [Waldrop] “AI is a programming style, where programs operate on data according to rules in order to accomplish goals” [W.A. Taylor]
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“AI is the activity of providing such machines as computers with the ability to display behavior that would be regarded as intelligent if it were observed in humans” [R. Mc Leod]“Expert system is a computer program using expert knowledge to attain high levels of performance in a narrow problem area” [D.A. Waterman]“Expert system is a caricature of the human expert, in the sense that it knows almost everything about almost nothing” [A.R. Mirzai] AI is changing rapidly, these definitions are already obsolete!
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Knowledge acquisition and representation has limited the application of AI theories (shortcoming of symbolisms)
SC has become a part of “modern AI”
Researchers have directed their attention toward biologically inspired methodologies such as brain modeling, evolutionary algorithm and immune modeling
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These new paradigms simulate chemico-biological mechanisms responsible for natural intelligence generationSC and AI share the same long-term goal: build and understand machine intelligenceAn intelligent system can for example sense its environment (perceive) and act on its perception (react)SC is evolving under AI influences that sprang from cybernetics (the study of information and control in human and machines)
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Neural Network (NN)
Imitation of the natural intelligence of the brainParallel processing with incomplete informationNerve cells function about 106 times slower than electronic circuit gates, but human brains process visual and auditory information much faster than modern computersThe brain is modeled as a continuous-time non linear dynamic system in connectionist architectures • Connectionism replaced symbolically structured representationsDistributed representation in the form of weights between a massive set of interconnected neurons
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Fuzzy set theory
Human brains interpret imprecise and incomplete sensory information provided by perceptive organsFuzzy set theory provides a systematic calculus to deal with such information linguisticallyIt performs numerical computation by using linguistic labels stimulated by membership functionsIt lacks the adaptability to deal with changing external environments ==> incorporate NN learning concepts in fuzzy inference systems: NF modeling
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Evolutionary computation
Natural intelligence is the product of millions of years of biological evolutionSimulation of complex biological evolutionary processesGA is one computing technique that uses an evolution based on natural selectionImmune modeling and artificial life are similar disciplines based on chemical and physical lawsGA and SA population-based systematic random search (RA) techniques
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NF and SC characteristics
With NF modeling as a backbone, SC can be characterized as:
Human expertise (fuzzy if-then rules)
Biologically inspired computing models (NN)
New optimization techniques (GA, SA, RA)
Numerical computation (no symbolic AI so far, only numerical)
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NF and SC Characteristics Contd…
New application domains: mostly computation intensive like adaptive signal processing, adaptive control, nonlinear system identification etcModel free learning:-models are constructed based on the target system onlyIntensive computation: based more on computation
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NF and SC Characteristics Contd…
Fault tolerance: deletion of a neuron or a rule does not destroy the system. The system performs with lesser quality
Goal driven characteristics:- only the goal is important and not the path.
Real world application:- large scale, uncertainties
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summary
SC is evolving rapidly
New techniques and applications are constantly being proposed
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