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Soft Computing - Overview Complete ebook and lecture notes pdf
Pankaj Yadav

Soft Computing - Overview Complete ebook and lecture notes pdf

Pankaj Yadav | 16-Jan-2016 |
Introduction , Fuzzy Systems , Evolutionary Computation , Neural networks , Machine Learning , Probabilistic Reasoning , Fuzzy Optimization , Fuzzy Sets , Fuzzy Multi-Criteria Decision Making , Fuzzy Mathematical Programming , Fuzzy Linear Programming ,

Hi friends, here Pankaj Yadav uploaded notes for SOFT COMPUTING with title Soft Computing - Overview Complete ebook and lecture notes pdf. You can download this lecture notes, ebook by clicking on the below file name or icon.

I SoftComputing-Overview 1
1 Introduction 3
1.1 Guiding Principle of Soft Computing . . . . . . . . . . . . . . . . 3
1.2 Importance of Soft Computing . . . . . . . . . . . . . . . . . . . 4
1.3 The Contents of the Study . . . . . . . . . . . . . . . . . . . . . . 4
2 Fuzzy Systems 7
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 Fuzzy Numbers and Fuzzy Arithmetic . . . . . . . . . . . . . . . 13
2.5 Determination ofMembership Functions . . . . . . . . . . . . . . 13
2.5.1 Subjective evaluation and elicitation . . . . . . . . . . . . 14
2.5.2 Ad-hoc forms andmethods . . . . . . . . . . . . . . . . . 14
2.5.3 Converted frequencies or probabilities . . . . . . . . . . . 14
2.5.4 Physicalmeasurement . . . . . . . . . . . . . . . . . . . . 14
2.6 Membership Degrees Versus Probabilities . . . . . . . . . . . . . 14
2.7 Possibility Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.8 Fuzzy Expert Systems . . . . . . . . . . . . . . . . . . . . . . . . 17
2.9 Fuzzy Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.10 Fuzzy Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.11 DecisionMaking in Fuzzy Environment . . . . . . . . . . . . . . 23
2.12 FuzzyMathematical Programming . . . . . . . . . . . . . . . . . 24
2.13 Mailing Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.14 Main International Journals . . . . . . . . . . . . . . . . . . . . . 28
2.15 Web Pages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.16 Fuzzy Researchers . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3 Evolutionary Computation 33
3.1 Genetic Algorithm(GA) . . . . . . . . . . . . . . . . . . . . . . . 34
3.2 Evolutionary Programming (EP) . . . . . . . . . . . . . . . . . . 36
3.3 Evolution Strategies (ES) . . . . . . . . . . . . . . . . . . . . . . 39
3.4 Classifier Systems (CS) . . . . . . . . . . . . . . . . . . . . . . . 40
3.5 Genetic Programming (GP) . . . . . . . . . . . . . . . . . . . . . 41
4 Neural Networks 43
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2 Principles of Neural Networks . . . . . . . . . . . . . . . . . . . . 44
4.3 LearningMethods in NNs . . . . . . . . . . . . . . . . . . . . . . 46
4.4 Well-Known Kinds of NNs . . . . . . . . . . . . . . . . . . . . . . 47
5 Machine Learning 55
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.2 Three Basic Theories . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.3 Supervisedmachine Learning . . . . . . . . . . . . . . . . . . . . 57
5.4 ReinforcementMachine Learning . . . . . . . . . . . . . . . . . . 57
5.5 Unsupervisedmachine Learning . . . . . . . . . . . . . . . . . . . 57
6 Probabilistic Reasoning 59
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
6.2 Markov and Bayesian Networks . . . . . . . . . . . . . . . . . . . 60
6.3 Decision Analysis based on PR . . . . . . . . . . . . . . . . . . . 60
6.4 Learning Structure fromData . . . . . . . . . . . . . . . . . . . . 61
6.5 Dampster-Shaffer’s Theory . . . . . . . . . . . . . . . . . . . . . 61
7 Conclusion 63
II Fuzzy Optimization 65
8 Fuzzy Sets 67
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
8.2 Definition and Basic Properties . . . . . . . . . . . . . . . . . . . 68
8.3 Operations with Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . 71
8.4 Extension Principle . . . . . . . . . . . . . . . . . . . . . . . . . . 72
8.5 Binary and Valued Relations . . . . . . . . . . . . . . . . . . . . 74
8.6 Fuzzy Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
8.7 Fuzzy Extensions of Valued Relations . . . . . . . . . . . . . . . 79
8.8 Fuzzy Quantities and Fuzzy Numbers . . . . . . . . . . . . . . . 84
8.9 Fuzzy Extensions of Real Functions . . . . . . . . . . . . . . . . . 87
8.10 Higher Dimensional Fuzzy Quantities . . . . . . . . . . . . . . . . 92
8.11 Fuzzy Extensions of Valued Relations . . . . . . . . . . . . . . . 97
9 Fuzzy Multi-Criteria Decision Making 103
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
9.2 Fuzzy Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
9.3 Pareto-Optimal Decisions . . . . . . . . . . . . . . . . . . . . . . 105
9.4 Compromise Decisions . . . . . . . . . . . . . . . . . . . . . . . . 109
9.5 Generalized Compromise Decisions . . . . . . . . . . . . . . . . . 112
9.6 Aggregation of Fuzzy Criteria . . . . . . . . . . . . . . . . . . . . 116
9.7 Extremal Properties . . . . . . . . . . . . . . . . . . . . . . . . . 117
9.8 Application to Location Problem . . . . . . . . . . . . . . . . . . 118
9.9 Application in Engineering Design . . . . . . . . . . . . . . . . . 125
10 Fuzzy Mathematical Programming 129
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
10.2 Modelling Reality by FMP . . . . . . . . . . . . . . . . . . . . . 131
10.3 MP Problemwith Parameters . . . . . . . . . . . . . . . . . . . . 131
10.4 Formulation of FMP Problem . . . . . . . . . . . . . . . . . . . . 133
10.5 Feasible Solutions of the FMP Problem . . . . . . . . . . . . . . 134
10.6 Properties of Feasible Solution . . . . . . . . . . . . . . . . . . . 135
10.7 Optimal Solutions of the FMP Problem . . . . . . . . . . . . . . 144
11 Fuzzy Linear Programming 151
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
11.2 Formulation of FLP problem . . . . . . . . . . . . . . . . . . . . 151
11.3 Properties of Feasible Solution . . . . . . . . . . . . . . . . . . . 154
11.4 Properties of Optimal Solutions . . . . . . . . . . . . . . . . . . . 156
11.5 Extended Addition in FLP . . . . . . . . . . . . . . . . . . . . . 160
11.6 Duality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
11.7 SpecialModels of FLP . . . . . . . . . . . . . . . . . . . . . . . . 167
11.7.1 Interval Linear Programming . . . . . . . . . . . . . . . . 167
11.7.2 Flexible Linear Programming . . . . . . . . . . . . . . . . 170
11.7.3 FLP Problems with Interactive Fuzzy Parameters . . . . 172
11.7.4 FLP Problems with Centered Parameters . . . . . . . . . 174
11.8 Illustrative Examples . . . . . . . . . . . . . . . . . . . . . . . . . 176

 

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