Course Page‎ > ‎ Lecture Note Selection File type icon File name Description Size Revision Time User Ċ 02_Fundamentals_of_Neural_Network.pdf 415k v. 1 Jul 9, 2012, 11:48 PM Harish Rathod Ċ 03-Back_Propagation_Network.pdf 293k v. 1 Jul 9, 2012, 11:49 PM Harish Rathod Ċ 06-Fuzzy_Set_Theory.pdf 339k v. 1 Jul 21, 2012, 1:48 AM Harish Rathod Ċ 09_Genetic_Algorithms.pdf 276k v. 1 Jul 25, 2012, 1:10 AM Harish Rathod Ċ 09-Hybrid_Systems.pdf 353k v. 1 Jul 30, 2012, 10:59 PM Harish Rathod ć Back Propagation Network.pptx 70k v. 1 Jul 9, 2012, 11:49 PM Harish Rathod ć Fuzzy Set Theory.pptx 149k v. 1 Jul 21, 2012, 1:47 AM Harish Rathod ć GENETIC ALGORITHMS.pptx 84k v. 1 Jul 25, 2012, 1:10 AM Harish Rathod ć Hybrid Intelligent Systems.ppt 503k v. 1 Oct 15, 2012, 8:29 PM Harish Rathod ć Hybrid System.pptx 43k v. 1 Oct 15, 2012, 8:27 PM Harish Rathod J.S.R.Jang, C.T.Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI,.rar Download 7117k v. 1 Jul 21, 2012, 1:51 AM Harish Rathod ć Rough Set.pptx 97k v. 1 Oct 15, 2012, 8:28 PM Harish Rathod ć Soft Computing-173101.pptx 125k v. 1 Jul 9, 2012, 11:48 PM Harish Rathod ć UNIT-7.pptx 136k v. 1 Oct 15, 2012, 8:35 PM Harish Rathod

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2. Introduction To Soft Computing Pdf

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C++ Notes Pdf Download

Table of Contents

Introduction To Soft Computing Pdf

1. Chapter 1: Introduction
2. Chapter 2: Fuzzy Sets
   1. 2.1 Crisp Sets: A Review
   2. 2.2 Fuzzy Sets
   3. 2.3 Fuzzy Membership Functions
   4. 2.5 Fuzzy Relations
   5. 2.6 Fuzzy Extension Principle
3. Chapter 3: Fuzzy Logic
   1. 3.1 Crisp Logic: A Review
   2. 3.2 Fuzzy Logic Basics
   3. 3.4 Fuzzy Rules
   4. 3.5 Fuzzy Reasoning
4. Chapter 4: Fuzzy Inference Systems
   1. 4.6 Defuzzification of the Resultant Aggregate Fuzzy Set
   2. 4.7 Fuzzy Controllers
5. Chapter 5: Rough Sets
6. Chapter 6: Artificial Neural Networks: Basic Concepts
   1. 6.1 Introduction
   2. 6.2 Computation in Terms of Patterns
   3. 6.4 The Perceptron
   4. 6.5 Neural Network Architectures
   5. 6.6 Activation Functions
   6. 6.7 Learning by Neural Nets
7. Chapter 7: Pattern Classifiers
8. Chapter 8: Pattern Associators
   1. 8.1 Auto-associative Nets
   2. 8.2 Hetero-associative Nets
   3. 8.3 Hopfield Networks
   4. 8.4 Bidirectional Associative Memory
9. Chapter 9: Competitive Neural Nets
   1. 9.1 The MAXNET
   2. 9.2 Kohonen’s Self-organizing Map (SOM)
   3. 9.3 Learning Vector Quantization (LVQ)
   4. 9.4 Adaptive Resonance Theory (ART)
10. Chapter 10: Backpropagation
    1. 10.1 Multi-layer Feedforward Net
    2. 10.3 The Backpropagation Algorithm
11. Chapter 11: Elementary Search Techniques
    1. 11.2 State Space Search
    2. 11.3 Exhaustive Search
    3. 11.4 Heuristic Search
12. Chapter 12: Advanced Search Strategies
    1. 12.1 Natural Evolution: A Brief Review
    2. 12.2 Genetic Algorithms (GAs)
    3. 12.3 Multi-objective Genetic Algorithms
13. Chapter 13: Hybrid Systems
    1. 13.1 Neuro-genetic Systems
    2. 13.2 Fuzzy-neural Systems