JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

• 1. To know the Neural Networks and Feed Forward Networks, Learnig Methods
• 2. To acquire a knowledge about Associative Memories and Hopfield Networks
• 3. To understand the design of Fuzzy logic and hybrid systems
• 4. To know the Genetic algorithm and its applications

Course Outcomes

Students will be able to:

• 1. Apply a different learning methods for various applications
• 2. Utilize Associative Memories in various networks
• 3. Design a Fuzzy based systems
• 4. Use the Genetic algorithm for Various systems

UNIT - I

Fundamentals of Neural Networks & Feed Forward Networks: Basic Concept of Neural Networks, Human Brain, Models of an Artificial Neuron, Learning Methods, Neural Networks Architectures, Signal Layer Feed Forward Neural Network :The Perceptron Model, Multilayer Feed Forward Neural Network :Architecture of a Back Propagation Network(BPN), The Solution, Backpropagation Learning, Selection of various Parameters in BPN. Application of Back propagation Networks in Pattern Recognition & Image Processing.

UNIT - II

Associative Memories & ART Neural Networks: Basic concepts of Linear Associator, Basic concepts of Dynamical systems, Mathematical Foundation of Discrete-Time Hop field Networks(HPF), Mathematical Foundation of Gradient-Type Hopfield Networks, Transient response of Continuous Time Networks, Applications of HPF in Solution of Optimization Problem: Minimization of the Traveling salesman tour length, Summing networks with digital outputs, Solving Simultaneous Linear Equations, Bidirectional Associative Memory Networks; Cluster Structure, Vector Quantization, Classical ART Networks, Simplified ART Architecture.

UNIT - III

Fuzzy Logic & Systems: Fuzzy sets, Crisp Relations, Fuzzy Relations, Crisp Logic, Predicate Logic, Fuzzy Logic, Fuzzy Rule based system, Defuzzification Methods, Applications: Greg Viot’s Fuzzy Cruise Controller, Air Conditioner Controller.

UNIT - IV

Genetic Algorithms: Basic Concepts of Genetic Algorithms (GA), Biological background, Creation of Offsprings, Working Principle, Encoding, Fitness Function, Reproduction, Inheritance Operators, Cross Over, Inversion and Deletion, Mutation Operator, Bit-wise Operators used in GA, Generational Cycle, Convergence of Genetic Algorithm.

UNIT - V

Hybrid Systems:Types of Hybrid Systems, Neural Networks, Fuzzy Logic, and Genetic Algorithms Hybrid, Genetic Algorithm based BPN: GA Based weight Determination, Fuzzy Back Propagation Networks: LR-type fuzzy numbers, Fuzzy Neuron, Fuzzy BP Architecture, Learning in Fuzzy BPN, Inference by fuzzy BPN.

TEXT BOOKS:

• 1. Introduction to Artificial Neural Systems - J.M.Zurada, Jaico Publishers
• 2. Neural Networks, Fuzzy Logic & Genetic Algorithms: Synthesis & Applications -S.Rajasekaran, G.A. Vijayalakshmi Pai, PHI, New Delhi, July 2011.
• 3. Genetic Algorithms by David E. Gold Berg, Pearson Education India, 2006.

REFERENCE BOOKS:

• 1. Artificial Neural Networks - Dr. B. Yagananarayana, PHI, New Delhi, , 1999.
• 2. An introduction to Genetic Algorithms - Mitchell Melanie, MIT Press, 1998
• 3. Fuzzy Sets, Uncertainty and Information- Klir G.J. & Folger. T. A., PHI, Delhi, 1993.
• 4. Neural Networks & Fuzzy Sytems- Kosko.B., PHI, Delhi,1994. M.TECH.- II YEAR- I SEMESTER DIGITAL SYSTEMS & COMPUTER ELECTRONICS