# JNTU Syllabus for Neural Networks and Fuzzy Logic

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY

KAKINADA

IV Year B.Tech EEE I-Sem T P C

4+1* 0 4

NEURAL NETWORKS AND FUZZY LOGIC

Objective :

This course introduces the basics of Neural Networks and essentials of Artificial Neural Networks with

Single Layer and Multilayer Feed Forward Networks. Also deals with Associate Memories and introduces

Fuzzy sets and Fuzzy Logic system components. The Neural Network and Fuzzy Network system

application to Electrical Engineering is also presented. This subject is very important and useful for doing

Project Work.

Unit – I: Introduction to Neural Networks

Introduction, Humans and Computers, Organization of the Brain, Biological Neuron, Biological and

Artificial Neuron Models, Hodgkin-Huxley Neuron Model, Integrate-and-Fire Neuron Model, Spiking

Neuron Model, Characteristics of ANN, McCulloch-Pitts Model, Historical Developments, Potential

Applications of ANN.

Unit- II: Essentials of Artificial Neural Networks

Artificial Neuron Model, Operations of Artificial Neuron, Types of Neuron Activation Function, ANN

Architectures, Classification Taxonomy of ANN – Connectivity, Neural Dynamics (Activation and

Synaptic), Learning Strategy (Supervised, Unsupervised, Reinforcement), Learning Rules, Types of

Application

Unit–III: Single Layer Feed Forward Neural Networks

Introduction, Perceptron Models: Discrete, Continuous and Multi-Category, Training Algorithms: Discrete

and Continuous Perceptron Networks, Perceptron Convergence theorem, Limitations of the Perceptron

Model, Applications.

Unit- IV: Multilayer Feed forward Neural Networks

Credit Assignment Problem, Generalized Delta Rule, Derivation of Backpropagation (BP) Training,

Summary of Backpropagation Algorithm, Kolmogorov Theorem, Learning Difficulties and Improvements.

Unit V: Associative Memories

Paradigms of Associative Memory, Pattern Mathematics, Hebbian Learning, General Concepts of

Associative Memory (Associative Matrix, Association Rules, Hamming Distance, The Linear Associator,

Matrix Memories, Content Addressable Memory), Bidirectional Associative Memory (BAM) Architecture,

BAM Training Algorithms: Storage and Recall Algorithm, BAM Energy Function, Proof of BAM Stability

Theorem

Architecture of Hopfield Network: Discrete and Continuous versions, Storage and Recall Algorithm,

Stability Analysis, Capacity of the Hopfield Network

Summary and Discussion of Instance/Memory Based Learning Algorithms, Applications.

Unit – VI: Classical & Fuzzy Sets

Introduction to classical sets – properties, Operations and relations; Fuzzy sets, Membership,

Uncertainty, Operations, properties, fuzzy relations, cardinalities, membership functions.

UNIT VII: Fuzzy Logic System Components

Fuzzification, Membership value assignment, development of rule base and decision making system,

Defuzzification to crisp sets, Defuzzification methods.

UNIT VIII: Applications

Neural network applications: Process identification, control, fault diagnosis and load forecasting.

Fuzzy logic applications: Fuzzy logic control and Fuzzy classification.

TEXT BOOK:

1. Neural Networks, Fuzzy logic, Genetic algorithms: synthesis and applications by Rajasekharan and

Rai – PHI Publication.

2. Introduction to Neural Networks using MATLAB 6.0 – S.N.Sivanandam, S.Sumathi, S.N.Deepa, TMH,

2006

REFERENCE BOOKS:

1. Neural Networks – James A Freeman and Davis Skapura, Pearson Education, 2002.

2. Neural Networks – Simon Hakins , Pearson Education

3. Neural Engineering by C.Eliasmith and CH.Anderson, PHI

4. Neural Networks and Fuzzy Logic System by Bart Kosko, PHI Publications.