Soft Computing

Techniques and its Applications in Electrical Engineering

By: Devendra K. Chaturvedi

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Hardcover | 20 August 2008

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636 Pages

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Intuitive consciousness/ wisdom is also one of the frontline areas in soft computing, which has to be always cultivated by meditation. This book is an introduction to some new fields in soft computing with its principal components of fuzzy logic, ANN and EA and it is hoped that it would be quite useful to study the fundamental concepts on these topics for the pursuit of allied research.

The approach in this book is to provides an understanding of the soft computing field, to work through soft computing using examples, to integrate pseudo - code operational summaries and Matlab codes, to present computer simulation, to include real world applications and to highlight the distinctive work of human consciousness in machine.

"I believe the chapters would help in understanding not only the basic issues and characteristic features of soft computing, but also the aforesaid problems of CTP and in formulating possible solutions. Dr. Chaturvedi deserves congratulations for bringing out the nice piece of work." Sankar K. Pal, Director Indian Statistical Institute

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Non-Fiction Science Physics Electricity Mathematics Applied Mathematics Engineering & Technology Energy Technology & Engineering Electrical Engineering Computing & I.T.Computer Science Artificial Intelligence

Expert Systems & Knowledge-Based Systems Technology in General Engineering in General

Foreword	p. V
Preface	p. VII
Acknowledgements	p. XIII
Introduction to Soft Computing	p. 1
Introduction	p. 1
Importance of Soft Computing	p. 3
Main Components of Soft Computing	p. 4
Fuzzy Logic	p. 4
Artificial Neural Networks	p. 5
Introduction to Evolutionary Algorithms	p. 7
Hybrid Intelligent Systems	p. 8
Summary	p. 9
Bibliography and Historical Notes	p. 10
Exercises	p. 10
Life History of Brain	p. 11
Introduction	p. 11
Development of Brain with Age	p. 12
Technologies for Study the Details of Brain	p. 14
Electro Encephalo Graph (EEG)	p. 15
Computerized Axial Tomography (CAT)	p. 15
Positron Emission Tomography (PET)	p. 15
Magnetic Resonance Imaging (MRI)	p. 16
Magneto Encephalo Graphy (MEG)	p. 16
Brain Functioning	p. 16
Brain Structure	p. 17
Brainwaves to Study the State of Brain	p. 18
Summary	p. 21
Bibliography and Historical Notes	p. 22
Exercises	p. 22
Artificial Neural Network and Supervised Learning	p. 23
Introduction	p. 23
Comparison of Neural Techniques and Artificial Intelligence	p. 24
Artificial Neuron Structure	p. 24
Adaline	p. 28
ANN Learning	p. 38
Back-Propagation Learning	p. 39
Properties of Neural Networks	p. 45
Limitations in the Use of Neural Networks	p. 45
Summary	p. 48
Bibliography and Historical Notes	p. 49
Exercises	p. 49
Factors Affecting the Performance of Artificial Neural Network Models	p. 51
Network Complexity	p. 51
Neuron Complexity	p. 51
Number of Layers	p. 52
Number of Neurons in Each Layer	p. 53
Type and Number of Interconnecting Weights	p. 53
Problem Complexity	p. 54
Range of Normalization of Training Data	p. 54
Type of Functional Mapping	p. 55
Sequence of Presentation of Training Data	p. 60
Repetition of Data in the Training Set	p. 60
Permissible Noise in Data	p. 62
Learning Complexity	p. 63
Training Algorithms of ANN	p. 63
Selection of Error Functions	p. 81
Mode of Error Calculation	p. 82
Summary	p. 82
Bibliography and Historical Notes	p. 84
Exercises	p. 85
Development of Generalized Neuron and Its Validation	p. 87
Existing Neuron Model	p. 87
Development of a Generalized Neuron (GN) Model	p. 90
Advantages of GN	p. 93
Learning Algorithm of a Summation Type Generalized Neuron	p. 94
Benchmark Testing of Generalized Neuron Model	p. 98
Ex-OR Problem	p. 98
The Mackey-Glass Time Series	p. 102
Character Recognition Problem	p. 105
Sin(X1) * Sin(X2) Problem	p. 107
Coding Problem	p. 107
Generalization of GN model	p. 114
GN Model 1	p. 115
GN Model 2	p. 115
GN Model 3	p. 116
GN Model 4	p. 116
Discussion on Benchmark Testing	p. 117
Summary	p. 121
Exercises	p. 122
Applications of Generalized Neuron Models	p. 123
Application of GN Models to Electrical Machine Modeling	p. 123
GN Models	p. 123
Results	p. 125
Discussions	p. 125
Training Time and Data Required	p. 125
Fault Tolerant Capabilities	p. 125
Effect of Different Mappings on GN Models	p. 133
Effect of Different Normalizations on GNN Models	p. 136
Conclusions	p. 140
Electrical Load Forecasting Problem	p. 141
Litreture Review	p. 142
Short Term Load Forecasting Using Generalized Neuron Model	p. 145
Training of ANN and GN Model	p. 149
Testing of ANN and GNM	p. 152
Discussion on Training and Testing Results	p. 153
Load Frequency Control Problem	p. 155
Need of Load Frequency Control	p. 156
Requirements for Selecting Controller Strategy	p. 157
Modelling of Thermal Power Plant (Single Area System)	p. 158
Response of Load Frequency Control of an Isolated (Single Area) Power System	p. 163
Development of GN Based Load Frequency Controller	p. 176
Power System Stabilizer Problem	p. 186
Conventional PSS	p. 187
GN Based PSS and its Training	p. 188
Comparison of GN and ANN PSS	p. 190
Simulation Results of GN Based PSS	p. 190
Experimental Test	p. 194
Adaptive GN Based Power System Stabilizer	p. 198
Conclusions	p. 210
Aircraft Landing Control System Using GN Model	p. 210
Introduction	p. 211
Aircraft Landing System	p. 211
Drawbacks of Existing Landing Control System	p. 213
Mathematical Model Development of Aircraft During Landing	p. 214
Develpoment of Landing Control System Using GN Model	p. 215
Simulation Results	p. 218
Conclusions	p. 221
Bibliography and Historical Notes	p. 221
Introduction to Fuzzy Set Theoretic Approach	p. 223
Introduction	p. 223
Uncertainty and Information	p. 226
Types of Uncertainty	p. 228
Introduction of Fuzzy Logic	p. 230
Historical Development of Fuzzy Logic	p. 231
Difference Between Precision and Significance	p. 233
Fuzzy Set	p. 233
Operations on Fuzzy Sets	p. 235
Fuzzy Intersection	p. 235
Fuzzy Union	p. 235
Fuzzy Complement	p. 236
Combination	p. 237
Fuzzy Concentration	p. 237
Fuzzy Dilation	p. 237
Fuzzy Intensification	p. 238
¿-Cuts	p. 238
Fuzzy Quantifier/Modifier/Hedges	p. 239
Characteristics of Fuzzy Sets	p. 244
Normality	p. 244
Convexity	p. 244
Cross Over Point	p. 245
Fuzzy Singletone	p. 245
Height	p. 246
Cardinality	p. 246
Properties of Fuzzy Sets	p. 246
Commutative Property	p. 246
Associative Property	p. 246
Distributive Property	p. 246
Idem Potency	p. 247
Identity	p. 247
Involution	p. 247
Excluded Middle Law	p. 247
Law of Contradiction	p. 247
Demorgan's Law	p. 247
Transitive	p. 248
Fuzzy Cartesian Product	p. 249
Various Shapes of Fuzzy Membership Functions	p. 250
Methods of Defining of Membership Functions	p. 253
Fuzzy Compositional Operators	p. 254
Relation	p. 258
Representation Methods of Relations	p. 259
Fundamental Properties of a Relation	p. 260
Fuzzy Relation	p. 263
Operation of Fuzzy Relation	p. 266
Projection and Cylindrical Extension	p. 269
Approximate Reasoning	p. 271
Denazification Methods	p. 280
Fuzzy Rule Based System	p. 282
Summary	p. 283
Bibliography and Historical Remarks	p. 283
Exercises	p. 284
Applications of Fuzzy Rule Based System	p. 295
Introduction	p. 295
System's Modeling and Simulation Using Fuzzy Logic Approach	p. 296
Selection of Variables, their Normalization Range and the Number of Linguistic Values	p. 298
Selection of Shape of Membership Functions for Each Linguistic Value	p. 298
Determination of Overlapping of Fuzzy Sets	p. 299
Selection of Fuzzy Intersection Operators	p. 299
Selection of Fuzzy Union Operators	p. 299
Selection of Implication Methods	p. 302
Selection of Compositional Rule	p. 303
Selection of Defuzzification Method	p. 305
Steady State D.C. Machine Model	p. 307
Transient Model of D.C. Machine	p. 320
Conclusions	p. 327
Control Applications	p. 330
Adaptive Control	p. 332
PID Control System	p. 335
Fuzzy Control System	p. 335
Power System Stabilizer Using Fuzzy Logic	p. 337
Summary	p. 359
Bibliography and Historical Notes	p. 360
Exercises	p. 361
Genetic Algorithms	p. 363
Introduction	p. 363
History of Genetics	p. 364
Genetic Algorithms	p. 366
Selection	p. 366
Crossover	p. 368
Mutation	p. 370
Survival of Fittest	p. 371
Population Size	p. 371
Evaluation of Fitness Function	p. 372
Effect of Crossover Probability on GA Performance	p. 373
Effect of Mutation Probability on GA Performance	p. 373
Main Components of GA	p. 375
Variants	p. 377
Applications of Genetic Algorithms	p. 379
Summary	p. 379
Bibliography and Historical Notes	p. 380
Exercises	p. 380
Applications of Genetic Algorithms to Load Forecasting Problem	p. 383
Introduction	p. 383
Introduction to Simple Genetic Algorithms	p. 384
Crossover Operation	p. 385
Mutation	p. 386
Population Size (Pop Size)	p. 387
Development of Improved Genetic Algorithm (IGM)	p. 387
Basis of Variation of Pc, Pm and Popsize	p. 388
Development of Fuzzy System	p. 390
Application of Improved Genetic Algorithm (IGA) to Electrical Load Forecasting Problem	p. 390
Results	p. 393
Integrated Fuzzy GA Technique	p. 393
Development of Adaptive Fuzzy System	p. 395
Limitations of GA	p. 401
Summary	p. 402
Synergism of Genetic Algorithms and Fuzzy Systems for Power System Applications	p. 403
Introduction	p. 403
Transmission Planning, Pricing and Structure/Models of Indian Power Sector	p. 404
GA-Fuzzy System Approach for Optimal Power Flow Solution	p. 410
OPF Problem	p. 411
Synergism of GA-Fuzzy System Approach	p. 413
GA-Fuzzy System Approach for OPF Solution (GAF-OPF)	p. 413
Test Results	p. 416
Conclusions	p. 431
Transmission Pricing Model Under Deregulated Environment	p. 431
Introduction	p. 431
Marginal Cost Based Transmission Pricing Method	p. 434
Postage Stamp Method	p. 446
MW Mile Methods	p. 448
Hybrid Deregulated Transmission Pricing Model	p. 452
Conclusion	p. 456
Congestion Management Using GA-Fuzzy Approach	p. 457
Introduction	p. 457
Transmission Congestion Penalty Factors	p. 459
Proposed Methods for Congestion Management	p. 461
Test Results	p. 463
Conclusions	p. 466
Bibliography and Historical Notes	p. 471
Integration of Neural Networks and Fuzzy Systems	p. 479
Introduction	p. 479
Adaptive Neuro-Fuzzy Inference Systems	p. 481
Constraints of ANFIS	p. 484
HIV/AIDS Population Model Using Neuro-Fuzzy Approach	p. 484
Introduction	p. 485
Roots of HIV/AIDS	p. 485
Neuro-Fuzzy Approach of Modeling	p. 487
Conclusions	p. 495
Summary	p. 498
Bibliographical and Historical Notes	p. 498
Exercise	p. 498
ANN - GA-Fuzzy Synergism and Its Applications	p. 501
Introduction	p. 501
Training of ANN	p. 502
Advantages of GA	p. 503
ANN Learning Using GA	p. 504
Validation and Verification of ANN-GA Model	p. 505
Summary	p. 507
Bibliography and Historical Notes	p. 508
References	p. 509
Glossary	p. 557
Artificial Neural Network	p. 557
Fuzzy Systems	p. 558
Genetic Algorithms	p. 560
Appendices	p. 563
Power System Model and its Parameters	p. 563
Single Machine Infinite Bus System	p. 563
Multimachine Power System	p. 564
C-Code For Fuzzy System	p. 567
Introduction	p. 567
Program for Fuzzy Simulation	p. 567
Data For 26-Bus System	p. 589
Data For 6-Bus System	p. 593
Data For IEEE 30-Bus System	p. 595
Data For Modified IEEE 30-Bus System	p. 599
Data For Indian UPSEB 75-Bus System	p. 603
Index	p. 609
Table of Contents provided by Publisher. All Rights Reserved.

Soft Computing

Techniques and its Applications in Electrical Engineering

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

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