Neural Networks and Fuzzy Logic

Below is the syllabus for Neural Networks and Fuzzy Logic:-

Unit I: Fundamentals of Artificial Neural Networks

Introduction: Concepts of neural networks, Characteristics of Neural Networks, Applications of Neural Networks. Fundamentals of Neural Networks: The biological prototype, Neuron concept, Single-layer Neural Networks, Multi-Layer Neural Networks, terminology, Notation, and representation of Neural Networks, Training of Artificial Neural Networks. Representation of perceptron, perceptron learning and training, Classification, Linear Separability

Unit II: Neural Networks

Hopfield nets: Structure, training, and applications, Back Propagation: Concept, Applications, and Back Propagation Training Algorithms. Counter Propagation Networks: Kohonen Network, Grossberg Layer & Training, applications of counter propagation, Image classification.

Bi-directional Associative Memories: Structure, retrieving a stored association, encoding associations.

Unit III: Special Neural Networks

ART: ART architecture, ART classification operation, ART implementation, and characteristics of ART. Image Compression Using ART, Optical Neural Networks: Vector Matrix Multipliers, Hop field net using Electro-optical matrix multipliers, Holographic correlator, Optical Hopfield net using Volume Holograms, Cognitrons and Neocognitrons: structure and training.

Unit IV: Fuzzy Logic

Fuzzy Logic: Introduction to Fuzzy Logic, Classical and Fuzzy Sets: Overview of Classical Sets, Membership Function, Fuzzy rule generation, Operations on Fuzzy Sets: Compliment, Intersections, Unions, Combinations of Operations, Aggregation Operations, Fuzzy Arithmetic: Fuzzy Numbers, Linguistic Variables, Arithmetic Operations on Intervals & Numbers, Lattice of Fuzzy Numbers, Fuzzy Equations, Introduction of Neuro-Fuzzy Systems, Architecture of Neuro-Fuzzy Networks, Genetic Algorithms: genetic algorithm implementation in problem-solving and working of genetic algorithms evolving neural networks, Differential Evolution optimization for engineering problems.

Text Books:

1. Li Min Fu, “Neural Networks in Computer Intelligence”, McGraw-Hill, Inc.
2. S N Sivanandam, “Neural Networks using MATLAB 6.0”, TMH, 4^th. Reprint
3. S N Sivanandam, “Principles of Soft Computing”, 2^nd. Edition, Wiley, Reprint

Reference Books:

1. Simon Haykin, “Neural Networks: A Comprehensive Foundations”, Prentice-Hall International, New Jersey,
2. Freeman A. & D.M. Skapura, “Neural Networks: Algorithms, Applications, and Programming Techniques”, Addison Wesley, Reading, Mass, 2014.

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