T M. Sc.(Sem-II) (Computer Science) According to new CBCS syllabus w. e. f. 2019-20 Sardesai, A. creator text xxu Pune Vision publication 2019 1st ed. monographic eng

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358 p. : 21 cm Contents 1. Introduction to Soft Computing 1. Neural Networks 2. Fuzzy Logic: Definition, Applications 3. Genetic Algorithms: Definition, Applications 2. Neural Network 1. Introduction 2. Fundamental Concepts 3. Artificial Neurons, Neural Networks and Architectures 4. Geometry of Binary Threshold Neurons and their Networks 5. Learning and Memory 6. Linear Separability, Hebb Network, Perceptron Network 7. MSE Error Surface and its Geometry 8. Application of LMS to Noise Cancellation 3. Fuzzy Set Theory 1. Brief Review of Conventional Set Theory 2. Fuzzy Sets 3. Cartesian Product 4. Crisp Relations 5. Fuzzy Relations 6. Tolerance and Equivalence Relations 7. Fuzzy Tolerance and Equivalence Relations 8. Value Assignments 9. Membership Functions 10.Various Forms 11.Fuzzification 12.Defuzzification to Crisp Set 13.a-Cuts for Fuzzy Relations 14.Defuzzification to Scalars 15.Fuzzy Logic 16.Approximate Reasoning 17.Other Forms of Implication Operation 18.Natural Language 19.Linguistic Hedges 20.Fuzzy (Rule Based) System 21.Graphical Techniques of Inference 22.Membership Value Assignments 23.Inference 4. Genetic Algorithms 1. Introduction 2. What is Genetic Algorithm? 3. Why Genetic Algorithm? 4. Robustance of Traditional Optimization and Search Methods 5. Goals of Optimization 6. How are Genetic Algorithms Different from Traditional Methods? 7. Simple GA 8. Genetic Algorithms At Work – A Simulation By Hand 9. Grist for the Search Mill – Important Similarities 10.Similarity Templates (Schemata) 11.Learning the Lingo Soft computing FYMSc-Sem -II Computer science 005.15 SAR 200306 20200306161139.0