JNTUH B.Tech 4th Year 2 sem Electronics and Communication Engineering R13 (4-2) Artificial Neural Networks (Elective – III) R13 syllabus

JNTUH B.Tech 4th year (4-2) Artificial Neural Networks (Elective – III) gives you detail information of Artificial Neural Networks (Elective – III) R13 syllabus It will be help full to understand you complete curriculum of the year.

Course Objectives

The objectives of this course are to

• Understand the basic building blocks of artificial neural networks (ANNs)
• Understand the role of neural networks in engineering and artificial intelligence modelling
• Provide knowledge of supervised/unsupervised learning in neural networks Provide knowledge of single layer and multi layer perceptrons.
• To know about self-organizational maps and Hop field models.

UNIT -I

Introduction: A Neural Network, Human Brain, Models of a Neuron, Neural Networks viewed as Directed Graphs, Network Architectures, Knowledge Representation, Artificial Intelligence and Neural Networks Learning Process: Error Correction Learning, Memory Based Learning, Hebbian Learning, Competitive, Boltzmann Learning, Credit Assignment Problem, Memory, Adaption, Statistical Nature of the Learning Process

UNIT -II

Single Layer Perceptrons: Adaptive Filtering Problem, Unconstrained Organization Techniques, Linear Least Square Filters, Least Mean Square Algorithm, Learning Curves, Learning Rate Annealing Techniques, Perceptron —Convergence Theorem, Relation Between Perceptron and Bayes Classifier for a Gaussian Environment

Multilayer Perceptron: Back Propagation Algorithm XOR Problem, Heuristics, Output Representation and Decision Rule, Computer Experiment, Feature Detection

UNIT -III

Back Propagation: Back Propagation and Differentiation, Hessian Matrix, Generalization, Cross Validation, Network Pruning Techniques, Virtues and Limitations of Back Propagation Learning, Accelerated Convergence, Supervised Learning.

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TEXT BOOK

• Neural Networks a Comprehensive Foundations, Simon Haykin, PHI edition.

REFERENCE BOOKS

• Artificial Neural Networks – B. Vegnanarayana Prentice Hall of India P Ltd 2005
• Neural Networks in Computer Inteligance, Li Mm Fu TMH 2003
• Neural Networks -James A Freeman David M S Kapura Pearson Education 2004.
• Introduction to Artificial Neural Systems Jacek M. Zurada, JAICO Publishing House Ed. 2006.

Course Outcomes

After the course the student should be able to:

• Explain the function of artificial neural networks of the Back-prop, Hopfield and SCM type
• Explain the difference between supervised and unsupervised learning
• Describe the assumptions behind, and the derivations of the ANN algorithms dealt with in the course
• Give example of design and implementation for small problems
• Implement ANN algorithms to achieve signal processing, optimization, classification and process modeling.

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