JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

• To cater the knowledge of Neural Networks and Fuzzy Logic Control and use these for controlling real time systems.
• To expose the students to the concepts of feed forward Neural Networks and about feedback Neural Networks
• To teach about the concepts of Fuzziness involved in various systems and comprehensive knowledge of Fuzzy logic control and to design the Fuzzy control

Course Outcomes

On successful completion of this course students should be able to:

• Understand the concepts of feed forward neural networks.
• Acquire adequate knowledge about feedback neural networks.
• Acquire the concept of fuzziness involved in various systems.
• Acquire knowledge about fuzzy set theory.

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 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 - III

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 - IV

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.

UNIT - V

Fuzzy Logic System: Introduction to classical sets - properties, Operations and relations; Fuzzy sets, Membership, Uncertainty, Operations, properties, fuzzy relations, cardinalities, membership functions. Fuzzification, Membership value assignment, development of rule base and decision-making system,

R19 M.TECH. SYSTEMS AND SIGNAL PROCESSING

Defuzzification to crisp sets, Defuzzification methods. Applications Neural network applications: Process identification, Function Approximation, control and Process Monitoring, 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 Artificial Neural Systems - Jacek M. Zuarda, Jaico Publishing House, 1997.

REFERENCE BOOKS:

• 1. Neural and Fuzzy Systems: Foundation, Architectures and Applications, - N. Yadaiah and S. BapiRaju, Pearson Education.
• 2. Neural Networks JamesA Freeman and Davis Skapura, Pearson, 2002.
• 3. Neural Networks SimonHykins , Pearson Education
• 4. Neural Engineering by C. Eliasmith and CH. Anderson, PHI
• 5. 5.Neural Networks and Fuzzy Logic System by Bork Kosko, PHI Publications R19 M.TECH. SYSTEMS AND SIGNAL PROCESSING M.TECH.- I YEAR- II SEMESTER SYSTEMS AND SIGNAL PROCESSING