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Kecman V. — Learning and soft computing. Support vector machines, neural networks, and fuzzy logic models
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Название: Learning and soft computing. Support vector machines, neural networks, and fuzzy logic modelsАвтор: Kecman V. Аннотация: This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial timeseries analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.
Язык: Рубрика: Computer science /AI, knowledge /Статус предметного указателя: Готов указатель с номерами страниц ed2k: ed2k stats Год издания: 2001Количество страниц: 541Добавлена в каталог: 16.05.2005Операции: Положить на полку |
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177 ABC adaptive backthrough control 429—449 ABC adaptive backthrough control, ABC of time-variant plant 440—443 ABC adaptive backthrough control, back propagation through a plant 427—428 Activation function 15—17 259 275—290 ADALINE 213 Additive noise 124 Animation 470—474 Approximating function 126 Approximation 29 34—41 Approximation error 134 136 Asymptotic consistency 131 Attribute See membership function Back propagation through a plant 427—428 Bayes decision criterion 71 78 Bayes risk 71 86 Bayesian classification 77—81 Best approximation 29 BFGS optimization method 488 Bias (offset, threshold) in NN 15 150 157 181 182 196 206 289 Bias-variance 40 136 268—274 Binary classification 71 Bipolar sigmoidal function 259 Canonical hyperplane 152 Classification 68 149 162 166 Classification, binary 71 Classification, dichotomization 91 Classifiers, parametric 92 Classifiers, template matching 101 Composition in FL 380—382 Computer graphics 463—480 Conjugate gradient method 430 489—494 Consistent estimators 275 Covariance matrix 93 334 341 529 Crafting sigmoidal AF (learning) 280—283 Cross-validation 40 137 269 272 Davidon — Fletcher — Powell method 487 Decision boundary 151 Decision regions 70 88 Defuzzification methods 393 Defuzzification methods, center-of-area 393 Defuzzification methods, center-of-gravity 393 Defuzzification methods, first-of-maxima 393 Defuzzification methods, middle-of-maxima 393 Degree of belonging 372 376 Delta signal, 234 257 Design matrix 35 Dichotomization 91 Discriminant function 89 Discriminant function for normally distributed classes 93—95 Distal teacher 426 428 EBP error back propagation 255—266 Empirical risk minimization ERM 130 Epoch 6 208 230 Equality of NNs arid FLMs 396 Error correction learning 194 204 234 236 Error signal term (\breve{S} signal) 234 257 Error stopping function 292 Error surface 44—53 302 484 Estimation error 135 Evolutionary computing 496—504 Facial animation 473 FAM, fuzzy additive model 404—410 Financial time series 449—463 Fletcher — Powell method 487 Fletcher — Reeves CG method 492 Fourier series and NN 47 Fuzzy logic systems, composition 380—382 Fuzzy logic systems, defuzzification 391—394 Fuzzy logic systems, defuzzification, center-of-area 393 Fuzzy logic systems, defuzzification, center-of-gravity 393 Fuzzy logic systems, defuzzification, first-of-maxima 393 Fuzzy logic systems, defuzzification, middle-of-maxima 393 Fuzzy logic systems, degree of belonging 372 376 Fuzzy logic systems, design steps for FL models 405 Fuzzy logic systems, fuzzification 385 391 Fuzzy logic systems, fuzzy additive models (FAM) 386 404—410 Fuzzy logic systems, IF-THEN rules 378 Fuzzy logic systems, implication 383—385 Fuzzy logic systems, inference 382—391 Fuzzy logic systems, membership function 21—24 367—371 Fuzzy logic systems, normal f. sets 368 Fuzzy logic systems, not-normal f. sets 368 Fuzzy logic systems, possibility degree 376 Fuzzy logic systems, relational matrix 376—382 Fuzzy logic systems, relations 374 Fuzzy logic systems, rule explosion 408 Fuzzy logic systems, S-norm 373 Fuzzy logic systems, set operations 371 Fuzzy logic systems, sets 367 Fuzzy logic systems, surface of knowledge 394—396 Fuzzy logic systems, T-norm 373 Fuzzy logic systems, trapezoidal membership function 371 Fuzzy logic systems, triangular membership function 371 Gauss — Newton method 495 Generalization error 134 Generalization of NNs and SVMs 40 269 Generalized delta ( 260 263 Generalized least squares 495 Genetic algorithms 496—504 Geometry of learning 277—288 Gradient method 49 54—60 230—237 301—302 518 Gramm — Schmidt orthogonalization 348 Graphics by RBF networks 463—480 Green's function 320 Growth function 144 Hessian matrix 57 229 296 301 485 495 Human animation 470—474 Hypothesis space 134 Ideal control 421 IF-THEN rules 378 Ill-posed problem 202 314 Indicator function 138 150 Insensitivity or s zone 177 Interpolation 34—41 Jacobian 428—430 Karash — Kuhn — Tucker condition 156 kernels 170 Key learning theorem 131 Kolmogorov theorem 13 Lagrangian, dual 156 163 172 180 Lagrangian, primal 156 163 172 180 Learning 61 Learning fuzzy rules (LFR) 396 Learning machine 126 Learning, 1. by subset selection 146 334 353 Learning, 1. of linear neuron weights (5 methods) 225 Learning, 1. rate 194 296 Learning, momentum term 296—301 Learning, moving center learning 337 Levenberg — Marquardt method 495 Likelihood ratio 78 Linear dynamic system 223 Linear neuron 213 Linear programming (LP) 353—358 Linear separability 202 LMS learning algorithm 234 Logistic (unipolar sigmoidal) function 259 Loss function 81 84 126 Lp norms 28—31 512 Mahalanobis distance 94 100 MAP maximal-a-posteriori decision criterion 71 Margin 153 Matrix-inversion lemma 237 239 Maximal margin classifier 149 Maximal-a-priori decision criterion 71 Membership function 21—24 367—371 Mercer kernels 170 MLP multilayer perceptron 15—18 26 255 Momentum term 296—301 Morphing 466—470 Multiclass classification 80 NARMAX model 422 433 451 Nested set of functions 114 Newton — Raphson method 229 301—302 485 NNs based control 421 NNs based control, ABC of time-variant plant 440—443 NNs based control, adaptive backthrough control ABC 429—449 NNs based control, backpropagation through a plant 427—428 NNs based control, dead-beat controller 433 NNs based control, direct inverse modeling 423 NNs based control, distal teacher 426 428 NNs based control, errors, definition of 431 NNs based control, errors, definition of, controller error 431 NNs based control, errors, definition of, performance error 431 NNs based control, errors, definition of, predicted performance error 431 NNs based control, errors, definition of, prediction error 431 NNs based control, general learning architecture 423 NNs based control, ideal linear controller 421 NNs based control, IMC internal model control 431 NNs based control, indirect learning architecture 425 NNs based control, Jacobian of the plant 428—430 NNs based control, parallel model 422 NNs based control, series-parallel model 422 NNs based control, specialized learning architecture 425 Noise influence on estimation 220 224 Nonradial BFs 337 339 Norm 28—31 512 Normal equation 228 344 OLS orthogonal least squares 343 Orthogonalization 350—352 Overfitting 41 269 Parametric classifier 92 Penalty parameter C 163 Perceptron 194 Perceptron, convergence of the p. learning rule 199 Perceptron, p. learning algorithms 204 Polak — Ribiere CG method 493 Possibility degree 376 Powell's quadratic approximation 58—61 Projection matrix 348 Quadratic programming 156—158 163—165 172—173 180—181 Quasi — Newton methods 486 Radial basis functions (RBFs) network 15—18 26 33—41 313—358 463 478 Regression 62—68 176 354—357 515 Regularization 314 Regularization parameter 137 320 329 Reproducing kernels 170 Ridge regression 137 Risk 85 RLS recursive-least-squares 237—241 Rule explosion 408 Second order optimization methods 483—496 Share market 450 Sigmoidal functions, bipolar s. f. 259 Sigmoidal functions, logistic (unipolar) function 259 Similarity between RBFs and FLMs 395—404 Soft margin 162 SRM, structural risk minimization 145 161 Stabilizer (in RBFs network) 320 329 Subset selection 146 334 353 Support vector 157 Support vector machines, SVMs 148 Support vector machines, SVMs, for classification 149 162 166 Support vector machines, SVMs, for regression 176 Surface of knowledge 394—396 System of linear equations 505 Underfitting 269 Uniform convergence 131 Universal approximation 36—37 Universe of discourse 367 Variable metric method 486 Variance 134—136 VC dimension 138 Vectors and matrices 510—514 Weight decay 137 Weights, geometrical meaning of weights 14 16 280—283 Weights, initialization 290
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