JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives

Student will be able to

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

Course Outcomes

On successful completion of this course, it is expected that students should be

able to

• 1. Understand the concepts of feed forward neural networks.
• 2. Acquire adequate knowledge about feedback neural networks.
• 3. Acquire the concept of fuzziness involved in various systems.
• 4. 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 NeuronModel, 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 ofNeuron Activation Function, ANN Architectures, Classification Taxonomy of ANN – Connectivity, NeuralDynamics (Activation and Synaptic), Learning Strategy (Supervised, Unsupervised, Reinforcement), LearningRules, 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 Back propagation (BP) Training, Summary of Backpropagation Algorithm, Kolmogorov Theorem, LearningDifficulties and Improvements. 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 BAMStability Theorem Architecture of Hopfield Network: Discrete and Continuous versions, Storage and Recall Algorithm, StabilityAnalysis, Capacity of the Hopfield Network.

UNIT - IV

Self-Organizing Maps (SOM) and Adaptive Resonance Theory (ART): Introduction, Competitive Learning, Vector Quantization, Self-Organized Learning Networks, Kohonen Networks, Training

R19 M.TECH EIE

Algorithms, Linear VectorQuantization, Stability-Plasticity Dilemma, Feed forward competition, Feedback Competition, Instar, Outstar, ART1, ART2, Applications. Classical and Fuzzy Sets: Introduction to classical sets - properties, Operations and relations; Fuzzy sets, Membership, Uncertainty, Operations, properties, fuzzy relations, cardinalities, membership functions.

UNIT - V

Fuzzy Logic System Components: Fuzzification, Membership value assignment, development of rule base and decision-making system, 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 BOOKS:

• 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.

REFERENCES:

• 1. Neural and Fuzzy Systems: Foundation, Architectures and Applications, - N. Yadaiah and S. Bapi Raju, Pearson Education.
• 2. Neural Networks – James A Freeman and Davis Skapura, Pearson, 2002.
• 3. Neural Networks – Simon Haykin Pearson Education
• 4. Neural Engineering by C. Eliasmith and CH. Anderson, PHI
• 5. Neural Networks and Fuzzy Logic System by Bork Kosko, PHI Publications R19 M.TECH EIE M.TECH.- I YEAR- I SEMESTER ELECTRONICS AND INSTRUMENTATION ENGINEERING