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ECE407 NEURAL NETWORKS & FUZZYCONTROL

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Reference Books

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Mode of Evaluation

Component Weightage

CAT I 15

CAT II 15

Quiz I 5Quiz II 5

Assignment 10

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Guidelines for Assignment

Option 1:Simulation of any algorithm discussed in theclass. It has to be application oriented not a merenumerical problem.

Option 2 :Refer a journal paper and explain the algorithmand how it is applied . Also Solve three numericalproblems other than what we have solved in theclass and attach a copy of the journal paper

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Guidelines for Assignments

Priory inform the topic to avoid repetition. Not more than two per team.

It has to be submitted along with a report It has to submitted in time. All the members should come for submission Short Viva-Voce will be taken individually

during the submission.

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Introduction

Why ANN Some tasks can be done easily (effortlessly) by humans but

are hard by conventional paradigms on Von Neumannmachine with algorithmic approach

Pattern recognition (old friends, hand-written characters) Content addressable recall Approximate, common sense reasoning (driving, playing

piano, baseball player) These tasks are often ill-defined, experience based, hard to

apply logic

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Introduction

Von Neumann machine -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- One or a few high speed (ns)

processors with considerablecomputing power

One or a few shared highspeed buses forcommunication

Sequential memory access byaddress

Problem-solving knowledge isseparated from the computingcomponent

Hard to be adaptive

Human Brain-------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Large # (10 11 ) of low speed

processors (ms) with limitedcomputing power

Large # (10 15) of low speedconnections

Content addressable recall(CAM)

Problem-solving knowledgeresides in the connectivity ofneurons

Adaptation by changing theconnectivity

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Biological neural activity

Each neuron has a body , an axon , and many dendrites Can be in one of the two states: firing and rest. Neuron fires if the total incoming stimulus exceeds the threshold

Synapse : thin gap between axon of one neuron and dendrite of another. Signal exchange Synaptic strength/efficiency

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Introduction

What is an (artificial) neural network A set of nodes (units, neurons, processing elements)

Each node has input and output Each node performs a simple computation by its node

function Weighted connections between nodes

Connectivity gives the structure/architecture of the net

What can be computed by a NN is primarily determinedby the connections and their weights A very much simplified version of networks of neurons

in animal nerve systems

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IntroductionANN

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Nodes

input output node function

Connections connection strength

Bio NN--------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Cell body signal from other neurons firing frequency firing mechanism

Synapses synaptic strength

Highly parallel, simple local computation (at neuron level)achieves global results as emerging property of the interaction

(at network level) Pattern directed (meaning of individual nodes only in the context

of a pattern) Fault-tolerant/graceful degrading Learning/adaptation plays important role.

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Characteristics that can be Extracted

More Complex Network Non Linear Network

Highly Parallel NetworkIt Cannot decide individually, a group decides and fins the solution

Benefits includes Plasticity or Adaptability Not Robust Ability to Learn Different Learning Laws

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Neural Networks

Nervous System

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Properties & Capabilities of NN

Non Linearity Artificial Neuron may be Linear / Non Linear Made up of an interconnection of Non Linear

Neurons Distributed throughout the Network Physical mechanism responsible for the

generation of Input Signal

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Properties & Capabilities of NN

Input Output Mapping Unique Input signal and its Desired responses Minimize the differences between Actual and

Desired Responses Non Parametric estimation with statisitcal

criterion Supervised Learning Paradigm

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Properties & Capabilities of NN

Adaptivity Capability to adapt their synaptic weights to

changes in the environment. Real time synatpic weight change in a non

stationary environment Applications adaptive signal processing, adaptive

control , adaptive pattern classification.

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Properties & Capabilities of NN

Evidential Response Not only which output or pattern but also

confidence in the decision made. Contextual Information

Global activity of all other neurons Fault tolerance

_ Capable of robust Computation

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Properties & Capabilities of NN

VLSI Implementability Uniformity of Analysis & Design

Neurobiological Analogy