JNTU Syllabus for Neural Networks and Fuzzy Logic

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.

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