Soft computing and intelligent systems design: theory, tools, and applications/ (Record no. 4106)
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000 -LEADER | |
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fixed length control field | 12718cam a22001814a 4500 |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9788131723241 |
040 ## - CATALOGING SOURCE | |
Transcribing agency | CUS |
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.3 |
Item number | KAR/S |
100 1# - MAIN ENTRY--PERSONAL NAME | |
Personal name | Karray, Fakhreddine O. |
245 10 - TITLE STATEMENT | |
Title | Soft computing and intelligent systems design: theory, tools, and applications/ |
Statement of responsibility, etc. | Fakhreddine O. Karray and Clarence de Silva. |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
Place of publication, distribution, etc. | UP : |
Name of publisher, distributor, etc. | Pearson India, |
Date of publication, distribution, etc. | 2004. |
300 ## - PHYSICAL DESCRIPTION | |
Extent | xxii,560p. : |
Other physical details | ill. ; |
Dimensions | 24cm. |
504 ## - BIBLIOGRAPHY, ETC. NOTE | |
Bibliography, etc | Includes bibliographical references and index. |
505 ## - FORMATTED CONTENTS NOTE | |
Formatted contents note | Part I Fuzzy logic and fuzzy control<br/>Chapter 1 Introduction to intelligent systems<br/>and soft computing<br/>Introduction<br/>Intelligent systems<br/>1.2.1 Machine intelligence<br/>1.2.2 Meaning of intelligence<br/>1.2.3 Dynamics of intelligence<br/>1.2.4 Intelligent machines<br/>Knowledge-based systems<br/>1.3.1 Architectures of knowledge-based systems<br/>1.3.2 Production systems<br/>1.3.2.1 Reasoning strategies<br/>1.3.2.2 Conflict resolution methods<br/>1.3.3 Frame-based systems<br/>1.3.4 Blackboard systems<br/>1.3.5 Object-oriented programming<br/>1.3.6 Expert systems<br/>1.3.6.1 Development of an expert system<br/>1.3.6.2 Knowledge engineering<br/>1.3.6.3 Applications Knowledge representation and processing<br/>1.4.1 Semantic networks<br/>1.4.2 Crisp logic<br/>1.4.2.1 Crisp sets<br/>1.4.2.2 Operations of sets<br/>1.4.2.3 Logic<br/>1.4.2.4 Correspondence between sets and logic 32<br/>1.4.2.5' Logic processing (reasoning and inference) 33<br/>1.4.2.6 Laws of logic 33<br/>1.4.2.7 Rules of inference 35<br/>1.4.2.8 Propositional calculus and predicate calculus 37<br/>1.5 Soft computing 38<br/>1.5.1 Fuzzy logic 38<br/>1.5.2 Neural networks<br/>1.5.3 Genetic algorithms ^3<br/>1.5.4 Probabilistic reasoning 44<br/>1.5.5 Approximation and intelligence 45<br/>1.5.6 Technology needs 30<br/>Chapter 2 Fundamentals of fuzzy logic systems 57<br/>2.1 Introduction 37<br/>2.2 Background 38<br/>2.2.1 Evolution of fuzzy logic 60<br/>2.2.1.1 Popular applications 61<br/>2.2.2 Stages of development of an intelligent product 63<br/>2.2.3 Use of fuzzy logic in expert systems 64<br/>2.3 Fuzzy sets ^3<br/>2.3.1 Membership function 66<br/>2.3.2 Symbolic representation 66<br/>2.4 Fuzzy logic operations 68<br/>2.4.1 Complement (negation, NOT) 69<br/>2.4.2 Union (disjunction, OR) 70<br/>2.4.3 Intersection (conjunction, AND) 72<br/>2.4.4 Basic laws of fuzzy logic 73<br/>2.5 Generalized fuzzy operations 76<br/>2.5.1 Generalized fuzzy complement 76<br/>2.5.2 Triangular norms 77<br/>2.5.2.1 T-norm (generalized intersection) 77<br/>2.5.2.2 S-norm or triangular conorm<br/>(generalized union) 78<br/>2.5.3 Set inclusion (A c B) 80<br/>2.5.3.1 Grade of inclusion 81<br/>2.5.4 Set equality (A = B) 82<br/>2.5.4.1 Grade of equality 82<br/>2.6 Implicatiion (if-then) 82<br/>2.6.1 Considerations of fuzzy implication 83<br/>2.7 Some definitions 89<br/>2.7.1 Height of a fuzzy set 89<br/>2.7.2 Support set 89<br/>2.7.3 a-cut of a fuzzy set 90<br/>2.7.4 Representation theorem 90<br/>2.8 Fuzziness and fuzzy resolution 91<br/>2.8.1 Fuzzy resolution 91<br/>2.8.2 Degree of fuzziness 93<br/>2.8.2.1 Measures of fuzziness 93<br/>2.9 Fuzzy relations 97<br/>2.9.1 Analytical representation of a fuzzy relation 99<br/>2.9.2 Cartesian product of fuzzy sets 100<br/>2.9.3 Extension principle 101<br/>2.10 Composition and inference 105<br/>2.10.1 Projection 105<br/>2.10.2 Cylindrical extension 109<br/>2.10.3 Join 115<br/>2,10.4 Composition 116<br/>2.10.4.1 Sup-product composition 116<br/>2.10.5 Compositional rule of inference 117<br/>2.10.5.1 Composition through matrix<br/>multiplication 119<br/>2.10.6 Properties of composition 121<br/>2.10.6.1 Sup-t composition 121<br/>2.10.6.2 Inf-s composition 121<br/>2.10.6.3 Commutativity 121<br/>2.10.6.4 Associativity 122<br/>2.10.6.5 Distributivity 123<br/>2.10.6.6 DeMorgan's Laws 123<br/>2.10.6.7 Inclusion 123<br/>2.10.7 Extension principle 125<br/>2.11 Considerations of fuzzy decision-making 126<br/>2.11.1 Extensions to fuzzy decision-making 127<br/>Chapters Fuzzy logic control 137<br/>3.1 Introduction<br/>3.2 Background ^38<br/>3.3 Basics of fuzzy control 141<br/>3.3.1 Steps of fuzzy logic control 145<br/>3.3.2 Composition using individual rules 146<br/>3.3.3 Defuzzification 151<br/>3.3.3.1 Centroid method 151<br/>3.3.3.2 Mean of maxima method 151<br/>3.3.3.3 Threshold methods 152<br/>3.3.3.4 Comparison of the defuzzification methods 152<br/>3.3.4 Fuzzification 153<br/>3.3.4.1 Singleton method 154<br/>3.3.4.2 Triangular function method 154<br/>3.3.4.3 Gaussian function method 155<br/>3.3.4.4 Discrete case of fuzzification 156<br/>3.3.5 Fuzzy control surface 156<br/>3.3.6 Extensions of Mamdani fuzzy control 162<br/>3.4 Fuzzy control architectures 162<br/>3.4.1 Hierarchical fuzzy systems 164<br/>3.4.2 Hierarchical model 166<br/>3.4.2.1 Feedback/filter modules 168<br/>3.4.2.2 Functional/control modules 169<br/>3.4.3 Effect of information processing 169<br/>3.4.4 Effect of signal combination on fuzziness 171<br/>3.4.5 Decision table approach for a fuzzy tuner 172<br/>3.5 Properties of fuzzy control 180<br/>3.5.1 Fuzzy controller requirements 180<br/>3.5.2 Completeness 181<br/>3.5.3 Continuity 181<br/>3.5.4 Consistency 182<br/>3.5.5 Rule validity 185<br/>3.5.6 Rule interaction 185<br/>3.5.7 Rule base decoupling 186<br/>3.5.7.1 Decision-making through a<br/>coupled rule base 187<br/>3.5.7.2 Decision-making through an<br/>uncoupled rule base 189<br/>3.5.7.3 Equivalence condition 190<br/>3.6 Robustness and stability 191<br/>3.6.1 Fuzzy dynamic systems 191<br/>3.6.2 Stability of fuzzy systems 192<br/>3.6.2.1 Traditional approach to stability<br/>analysis 192<br/>3.6.2.2 Composition approach to stability<br/>analysis 195<br/>3.6.3 Eigen-fuzzy sets 200<br/>3.6.3.1 Iterative method 200<br/>Part lI Connectionist modeling and neural networks 221<br/>Chapter 4 Fundamentals of artificial neural networks 223<br/>4.1 Introduction 223<br/>4.2 Learning and acquisition of knowledge 224<br/>4.2.1 Symbolic learning 224<br/>4.2.2 Numerical learning 224<br/>4.3 Features of artificial neural networks 226<br/>4.3.1 Neural network topologies 227<br/>4.3.1.1 The feedforward topology 227<br/>4.3.1.2 The recurrent topology 227<br/>4.3.2 Neural network activation functions 228<br/>4.3.3 Neural network learning algorithms 230<br/>4.3.3.1 Supervised learning 230<br/>4.3.3.2 Unsupervised learning 231<br/>4.3.3.3 Reinforcement learning 232<br/>4.4 Fundamentals of connectionist modeling 233<br/>4.4.1 McCulloch-Pitts models 233<br/>4.4.2 Perceptron 234<br/>4.4.3 Adaline 243<br/>4.4.4 Madaline 244<br/>Chapter 5 Major classes of neural networks<br/>5.1 Introduction<br/>5.2 The multilayer perceptron<br/>5.2.1 Topology<br/>5.2.2 Backpropagation learning algorithm<br/>5.2.3 Momentum<br/>5.2.4 Applications and limitations of MLP<br/>5.3 Radial basis function networks<br/>5.3.1 Topology<br/>5.3.2 Learning algorithm for RBF<br/>5.3.3 Applications<br/>5.4 Kohonen's self-organizing network<br/>5.4.1 Topology<br/>5.4.2 Learning algorithm<br/>5.4.3 Applications<br/>5.5 The Hopfield network<br/>5.5.1 Topology<br/>5.5.2 Learning algorithm<br/>5.5.3 Applications of Hopfield networks<br/>Industrial and commercial applications of ANN 281<br/>5.6.1 Neural networks for process monitoring<br/>and optimal control 282<br/>5.6.2 Neural networks in semiconductor<br/>manufacturing processes 282<br/>5.6.3 Neural networks for power systems 284<br/>5.6.4 Neural networks in robotics 285<br/>5.6.5 Neural networks in communications 286<br/>5.6.6 Neural networks in decision fusion and<br/>pattern recognition 288<br/>Chapter 6 Dynamic neural networks and their<br/>applications to control and chaos prediction 299<br/>6.1 Introduction 299<br/>6.2 Background 300<br/>6.2.1 Basic concepts of recurrent networks 300<br/>6.2.2 The dynamics of recurrent neural networks 301<br/>6.2.3 Architecture 301<br/>6.3 Training algorithms 304<br/>6.3.1 Backpropagation through time (BPTT) 304<br/>6.3.2 Real-time backpropagation learning 305<br/>6.4 Fields of applications of RNN 306<br/>6.5 Dynamic neural networks for identification and control 307<br/>6.5.1 Background 307<br/>6.5.2 Conventional approaches for identification and control 308<br/>6.5.2.1 Systems identification 310<br/>6.5.2.2 Adaptive control 311<br/>6.6 Neural network-based control approaches 313<br/>6.6.1 Neural networks for identification 315<br/>6.6.2 Neural networks for control 320<br/>6.6.2.1 Supervised control 320<br/>6.6.2.2 Inverse control 321<br/>6.6.2.3 Neuro-adaptive control 322<br/>6.7 Dynamic neural networks for chaos time series prediction <br/>6.7.1 Background 324<br/>6.7.2 Conventional techniques for chaos system prediction and control 324<br/>6.7.3 Artificial neural networks for chaos prediction 325<br/>6.7.3.1 Conventional feedforward networks 325<br/>6.73.2 Recurrent neural networks (RNNs)-based predictors 327<br/>Chapter 7 Neuro-fuzzy systems 337<br/>7.1 Introduction 337<br/>7.2 Background 338<br/>7.3 Architectures of neuro-fuzzy systems 339<br/>7.3.1 Cooperative neuro-fuzzy systems 340<br/>7.3.1.1 Neural networks for determining<br/>membership functions 341<br/>7.3.1.2 Adeli-Hung algorithm (AHA) 342<br/>7.3.1.3 Learning fuzzy rules using neural networks 343<br/>7.3.1.4 Learning in fuzzy systems using neural networks 344<br/>7.3.1.5 Identifying weighted fuzzy rules using neural networks <br/>7.3.2 Neural network-driven fuzzy reasoning 344<br/>7.3.3 Hybrid neuro-fuzzy systems 345<br/>7.3.3.1 Architecture of hybrid neuro-fuzzy systems 346<br/>7.3.3.2 Five-layer neuro-fuzzy systems 347<br/>7.3.3.3 Four-layer neuro-fuzzy systems (ANFIS) 350<br/>7.3.3.4 Three-layer neuro-fuzzy approximator 350<br/>7.4 Construction of neuro-fuzzy systems 355<br/>7.4.1 Structure identification phase 355<br/>7.4.1.1 Grid-type partitioning 355<br/>7.4.1.2 Clustering 356<br/>7.4.1.3 Scatter partitioning 357<br/>7 4^2 Parameter learning phase 357<br/>7.4.2.1 The backpropagation learning algorithm 358<br/>7.4.2.2 Hybrid learning algorithms 359<br/>Part III Evolutionary and soft computing 363<br/>Chapters Evolutionary computing 365<br/>8.1. Introduction 365<br/>8.2 Overview of evolutionary computing 366<br/>8.2.1 Evolutionary programming 369<br/>8.2.2 Evolutionary strategies 370<br/>8.2.3 Genetic programming 370<br/>8.2.4 Genetic algorithms 371<br/>8.3 Genetic algorithms and optimization 372<br/>8.3.1 Genotype 373<br/>8.3.2 Fitness function 374<br/>8.4 The schema theorem: the fundamental theorem of genetic algorithms 375<br/>8.5 Genetic algorithm operators 376<br/>8.5.1 Selection 377<br/>8.5.2 Crossover 377<br/>8.5.3 Mutation 378<br/>8.5.4 Mode of operation of GAs 378<br/>8.5.5 Steps for implementing GAs 381<br/>8.5.6 Search process in GAs 381<br/>8.6 Integration of genetic algorithms with neural networks 388<br/>8.6.1 Use of GAs for ANN input selection 388<br/>8.6.2 Using GA for NN learning 389<br/>8.7 Integration of genetic algorithms with fuzzy logic 390<br/>8.8 Known issues in GAs 391<br/>8.8.1 Local minima and premature convergence 391<br/>8.8.2 Mutation interference 392<br/>8.8.3 Deception 392<br/>8.8.4 Epistasis 392<br/>8.9 Population-based incremental learning 393<br/>8.9.1 Basics of PBIL 393<br/>8.9.2 Generating the population 393<br/>8.9.3 PBIL algorithm 394<br/>8.9.4 PBIL and learning rate 395<br/>8.10 Evolutionary strategies 395<br/>8.11 ES applications 400<br/>8.11.1 Parameter estimation 400<br/>8.11.2 Image processing and computer vision systems 400<br/>8.11.3 Task scheduling hy ES 400<br/>8.11.4 Mobile manipulator path planning by ES 400<br/>8.11.5 Car automation using ES 401<br/>Part IV Applications and case studies 405<br/>Chapter 9 Soft computing for smart machine design 407<br/>9.1 Introduction 407<br/>9.1.1 Intelligent machines 408<br/>9.1.2 Intelligent control 408<br/>9.1.3 Hierarchical architecture 409<br/>9.1.4 Development steps 411<br/>9.2 Controller tuning 413<br/>9.2.1 Problem formulation 414<br/>9.2.1.1 Rule base 415<br/>9.2.1.2 Compositional rule of inference 417<br/>9.2.2 Tuning procedure 418<br/>9.2.2.1 Rule dissociation 418<br/>9.2.2.2 Resolution relations 419<br/>9.2.2.3 Tuning inference 421<br/>9.2.2.4 Accuracy versus fuzzy resolution 421<br/>9.2.3 Illustrative example 422<br/>9.2.3.1 Resolution relation 423<br/>9.2.3.2 Stability region 426<br/>9.2.3.3 Tuning results 427<br/>9.3 Supervisory control of a fish processing machine 427<br/>9.3.1 Machine features 430<br/>9.3.2 Supervisory control system 432<br/>9.3.3 Information preprocessing 435<br/>9.3.3.1 Image preprocessing 435<br/>9.3.3.2 Servomotor response preprocessing 436<br/>9.3.3.3 Cutter load preprocessing 439<br/>9.3.3.4 Conveyor speed preprocessing 443<br/>9.3.4 Knowledge-based decision-making 443<br/>9.3.4.1 Knowledge acquisition 444<br/>9.3.4.2 Decision-making 446<br/>9.3.4.3 Servo tuning 449<br/>9.3.4.4 Product quality assessment 452<br/>9.3.4.5 Machine tuning 453<br/>9.3.5 System implementation 453<br/>9.3.5.1 System modules 455<br/>9.3.5.2 User interface of the machine 456<br/>9.3.6 Performance testing 457<br/>9.3.6.1 Servomotor tuning examples 457<br/>9.3.6.2 Machine tuning example 461<br/>9.3.6.3 Product quality assessment<br/>Chapter 10 Tools of soft computing in real-world applications <br/>Case study 1: Expert parameter tuning of DC motor controller Case study 2: Stabilizing control of a high-order power system<br/>by neural adaptive feedback linearization 497<br/>Case study 3; Soft computing tools for solving a class of facilities layout planning problem 510<br/>Case study 4: Mobile position estimation using an RBF network in CDMA cellular systems 522<br/>Case study 5: Learning-based resource optimization in ATM<br/>networks |
650 #0 - SUBJECT | |
Keyword | Soft computing. |
650 #0 - SUBJECT | |
Keyword | Expert systems (Computer science) |
700 ## - ADDED ENTRY--PERSONAL NAME | |
Personal name | Silva, Clarence de |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | General Books |
Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Full call number | Accession number | Date last seen | Koha item type |
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Central Library, Sikkim University | Central Library, Sikkim University | General Book Section | 03/07/2016 | 006.3 KAR/S | P40347 | 03/07/2016 | General Books |