ECE2_Neuraology 1-3

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Neural Network RKJHA

Lecture-01

NeuralNetwork:Work on artificial Neural N/W is commonly referred as Neural Networks

Motivation: Human brain computes in an entirely different fashion from the

computational computer .Though, it is highly complex, nonlinear, and has parallel

information processing system. It has the capability to organize its structural

constituent known as neurons so as to perform certain computation (e.g. Pattern

reorganization, perception e.t.c) and many times faster than the fastest digital

computer in existence today.

E.g human vision:-The visual system provides the representation of the

environment around us and more important information we need to interact with

the environment or recognizing a familiar face in an unfamiliar experience.

A brain has a great structure and the ability to build up its own rules through

what we usually refer as experience.

A developing neuron is synonymous with a plastic brain. Plasticity permits

developing neurons to (neuron system) adopt to is surrounding environment. In

general, also plasticity is an essential property for functioning of any machine for

information processing.

Neural Network is a machine that is designed to model the way in whichhuman brain performs a particular task or function

This N/W can be realized either by using electronic component or is

simulated in software on a digital computer.

An important class of neural network is one that performs useful computation

through a process of learning.

Neural network viewed as an adaptive machine can be defined as:A Neural Network is a massively parallel distributed processor made up of simple

processing units, which has a natural propensity for storing experimental

knowledge and making it available for use. It resembles the brain in two respects.


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Soft Computing

1. Knowledge is acquired by the N/W from its environment through a learning

process.

2. Inter neuron connection strengths, known as synaptic weights, are used to

store the acquired knowledge.

The procedure used to perform the learning process is called learning algorithm,

the function of which is to modify the synaptic weight of the network in an

orderly fashion to attain a desired design objective.

Benefits ofNeural Network:

Neural networks derive its computing power through:

1. Massively parallel distributed structure.

2. Its ability to learn and there for generalize.

Generalization refers to that the neural network can produce reasonable output for

inputs not encountered during training (learning).

**N.N can not provide solution by working individually. Rather it can be

integrated into a system engineering process.


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Lecture-02

Soft Computing

NN offers the following usefulproperties:

1. Nonlinearity:-A N can be linear as well as non linear.

2. Input-Output Mapping:- NN can be trained using sample data or task

example. Each example consists of a unique input signal and a

corresponding classical response. The network is trained by adjusting the

weights to minimize difference between classical o/p and actual o/p.

3. Adaptivity:- Neural network have a built in capability to adopt their

synaptic weights to changes in the surrounding environment.

In particular a neural network trained to operate in a specific environment

can be easily retrained to deal with minor changes in the operating

environmental condition. Also if NN is meant to function is a non stationary

environment, it can be designed to change its synaptic weight in real time. This

enables to make it a useful tool in adaptive pattern classification, adaptive

signal processing, and adaptive control.

** To realize the full benefit of adaptivity, the principle time constant of the

system should be long enough for the system to ignore spurious disturbances and

yet short enough to respond to meaning full changes in the environment.

4. Evidential response:- In context to pattern classification, a neural

network can be designed to provide information not only about which

pattern to select but also about the confidence in the discussion made:

The latter information is used to reject ambiguous patterns.

5. Contextual Information:- Knowledge is represented by the very

structure and actuation state of a neural network. Every neuron in the

network is potentially affected by the global activity of all other neurons

in the network.


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6. Fault Tolerance:- A neural network, implemented in hardware form has

the potential to be inherently fault tolerant or capable of about

computation, in the sense that is performance degrades gradually under

adverse operating conditions. Thus in principle a neural network exhibits

a graceful degradation in performance rather than catastrophic failure.

7. VLSI: Implementation:- The massively parallel nature of a neural

network makes it potentially fast for the computation of certain task. This

same feature makes a neural network will suited for implementation

using very large scale integrated technology.

8. Uniformity of Analysis & Design:- A neural n/w enjoys universality as

information processes. I.e. like same notion as used in all domainsinvolving application of NN.

9. Neurobiological Analogy:- The design of a neural network is motivated

by analogy with the brain, which is a living proof that fault tolerant

parallel processing is not only physically possible but also fast and

powerful.


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Lecture-03

Soft Computing

NeuralNetworks:

Brain contains about 1010

basic units called neurons. A neuron is a small cell that

receives electro-chemical signals from its various sources and in turn responds by

transmitting electrical impulses to other neurons.

Some neurons perform input operation referred to as afferent cell; some perform

output operation referred to as efferent cells, the remaining form a part of

interconnected network of neurons which are responsible for signal transformation

and storage of information.

Structure ofneuron:Graph

Dendrites: Behave as input channels, i.e. all inputs from other neurons arrive

through the dendrites.

Axiom: Is electrically active and serves as an output channel. There are the non

linear threshold devices which produce a voltage pulse called Action Potential. It

the cumulative inputs received by the soma raise the interval electric potential ofthe cell neuron as Membrane potential, then the neuron fires by propagating the

action potential shown the axiom to either or inhibit other neurons.

Synapse orSynaptic Junction:

The axiom terminates in a specialized contact called synapse or synaptic function

that connects axiom to dendrites links of other neurons.


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This synaptic function which is a very minute gap at the end of the dendrite

link contacts a neuron transmitter fluid.

The size of the synaptic junction or synapses is believed to be related to learning.

Thus, synapses with large area are thought to be exhibitory while those with small

area are believed to be inhibitory.

Model of A ArtificialNeuron:

Human brain is a highly interconnected network of simple processing elements

called neurons. The behavior of a neuron can be captured by a simple model

termed as artificial neuron.

In artificial neurons acceleration and retardation of modeled by weights. An

efficient synapse which transmits a stronger signal will have a corresponding larger

weight.

I = w1x1 + w2x2 + .+ wnxn


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