CS8086-SOFT COMPUTING Syllabus 2017 Regulation

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CS8086-SOFT COMPUTING Syllabus 2017 Regulation

SOFT COMPUTING Syllabus 2017 Regulation,CS8086-SOFT COMPUTING Syllabus 2017 Regulation

CS8086                                             SOFT COMPUTING                                   L T P C                                                                                                                            3 0 0 3

OBJECTIVES:

  • To learn the basic concepts of Soft Computing
  • To become familiar with various techniques like neural networks, genetic algorithms and fuzzy systems.
  • To apply soft computing techniques to solve problems.

UNIT I INTRODUCTION TO SOFT COMPUTING            9

Introduction-Artificial Intelligence-Artificial Neural Networks-Fuzzy Systems-Genetic Algorithm and Evolutionary Programming-Swarm Intelligent Systems-Classification of ANNs-McCulloch and Pitts Neuron Model-Learning Rules: Hebbian and Delta- Perceptron Network-Adaline Network-Madaline Network.

UNIT II ARTIFICIAL NEURAL NETWORKS                    9

Back propagation Neural Networks – Kohonen Neural Network -Learning Vector Quantization -Hamming Neural Network – Hopfield Neural Network- Bi-directional Associative Memory -Adaptive Resonance Theory Neural Networks- Support Vector Machines – Spike Neuron Models.

UNIT III FUZZY SYSTEMS                                               9

Introduction to Fuzzy Logic, Classical Sets and Fuzzy Sets – Classical Relations and Fuzzy Relations -Membership Functions -Defuzzification – Fuzzy Arithmetic and Fuzzy Measures -Fuzzy Rule Base and Approximate Reasoning – Introduction to Fuzzy Decision Making.

UNIT IV GENETIC ALGORITHMS                                   9

Basic Concepts- Working Principles -Encoding- Fitness Function – Reproduction -Inheritance Operators – Cross Over – Inversion and Deletion -Mutation Operator – Bit-wise Operators -Convergence of Genetic Algorithm.

UNIT V HYBRID SYSTEMS                                             9

Hybrid Systems -Neural Networks, Fuzzy Logic and Genetic -GA Based Weight Determination – LR-Type Fuzzy Numbers – Fuzzy Neuron – Fuzzy BP Architecture – Learning in Fuzzy BP- Inference by Fuzzy BP – Fuzzy ArtMap: A Brief Introduction – Soft Computing Tools – GA in Fuzzy Logic Controller Design – Fuzzy Logic Controller

                                                                                                     TOTAL : 45 PERIODS

OUTCOMES:

Upon completion of this course, the students should be able to

  • Apply suitable soft computing techniques for various applications.
  • Integrate various soft computing techniques for complex problems.

TEXT BOOKS:

  1. N.P.Padhy, S.P.Simon, “Soft Computing with MATLAB Programming”, Oxford University Press, 2015.
  2. S.N.Sivanandam , S.N.Deepa, “Principles of Soft Computing”, Wiley India Pvt. Ltd., 2nd Edition, 2011.
  3. S.Rajasekaran, G.A.Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithm, Synthesis and Applications “, PHI Learning Pvt. Ltd., 2017.

REFERENCES:

  1. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, ―Neuro-Fuzzy and Soft Computing, Prentice-Hall of India, 2002.
  2. Kwang H.Lee, ―First course on Fuzzy Theory and Applications, Springer, 2005.
  3. George J. Klir and Bo Yuan, ―Fuzzy Sets and Fuzzy Logic-Theory and Applications, Prentice Hall, 1996.
  4. James A. Freeman and David M. Skapura, ―Neural Networks Algorithms, Applications, and Programming Techniques, Addison Wesley, 2003.

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