|
 |
Авторизация |
|
 |
Поиск по указателям |
|
 |
|
 |
|
 |
 |
|
 |
|
Popovic D., Palit A.K. — Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications |
|
 |
Предметный указатель |
a priori probability 94
Abstraction (generalization) level 351
Accelerated backpropagation algorithm 99ff
Activation function 81
Activation function, selection 111
ADALINE 79 83
Adaptive evolutionary systems 4
Adaptive fitness function 322
Adaptive fuzzy logic system 232
Adaptive genetic algorithm 198 231 321ff
Adaptive genetic operators 322
Adaptive learning rate 99 246
Adaptive neuro-fuzzy approach 232
Adaptive operator selection 322
Adaptive parameter setting 322
Adaptive representation 322
Adatron 342ff
Affine wavelet decomposition 348
Age of chromosome 328
Age operator 328
Age structure of population 328
AIC see Akaike information criterion
Akaike information criterion (AIC) 45 109
AND fuzzy neuron 228ff
ANFIS architecture 9 226 230
Antecedent parameters of fuzzy clustering 185
Approximate reasoning 5
Approximated reasoning 5
AR model 27
ARIMA model 29 131 132
ARMA model 28
artificial intelligence 8 9
Association cortices 351
Associative memory 80 351
Associative memory networks 80
Auto-associative capabilities 88
Autocorrelation structure 107
Automated rules generation 157ff
Autonomous mental development 9
Autoregression model 27
Auxiliary genetic operators 201
Axons 81
B-spline functions 86
Backpropagation, learning 79 85
Backpropagation, networks 4 85
Backpropagation, through time 90
Backpropagation, training algorithm 95 237
Backpropagation, training implementation 97
Backpropagation, training of neuro-fuzzy network 234ff
Bayesian belief networks 5
Bayesian information criterion 45
Behavioural models 7 214
Belief theory 4
Bell-shaped function 86
Best approximator 129
Best generalization 120
Bi-directional associative memory 92
Bias 81
Bias error 120
Bias-Variance dilemma 119 120
Binary Hopfield net 89
Binary logic 143
Binary step function 81
Bivalent logic 5
Bivariate time series 33
Boinformatics 335
Box — Jenkins approach 84
C-means functional 178
Cai's fuzzy neuron 230
CARIMA model 71ff
CARMAX model 32 69
Cell body 80
Cellular encoding 308 312
Census I method 22
Census II method 22
Central motor cortex 351
Centre-of-gravity defuzzification 150
Cerebral cortex hierarchy 351
Chain of inferences 5
Chaotic configuration of data set 356
Chaotic time series 23 24
Chaotic time series, models 36
Characteristic features 18
Chromosome age 328
Chromosomes 6 310
Classifier systems 197
Cluster validity measure 181 280
Clustering, covariance matrix 185
Clustering, fuzziness parameter 181
Clustering, termination criterion 182
Clustering, theory 174
Clustering, using Kohonen networks 353ff
Cognition 4
Cognitive functions 350
Combined forecast 64
Combined fuzzy rule base 161
Combined modelling 136
Combining neural network and traditional methods 131ff
Compact modelling scheme 279ff
Compatible cluster merging 280
Competition concept 315
Competitive layer 92
Component level 322
Computation of Jacobian matrix 241
Computational Intelligence 3 8ff
Computing neuron 4 79
Conjunction operator 151
Connectionist encoding 308
Connectivity matrix 310
Constructive evolving of neural network 306
Context, layer 88
Context, nodes 87
Counterpropagation networks 80 92ff
Cover's theorem 337
Crisp function 148
Crisp input 146
Crisp logic 143
Crisp output 146
Crisp set 144
Cross validation 118
Crossover 6 7 195 196 201ff 322
Crossover, operators for real-coded GA 205ff
Crossover, probability 323
Crossover, rate 323
Data, clustering 279
Data, fuzzification 159
Data, matrix 174
Data, mining 10 11
Data, normalization 104
Data, preparation for forecasting 103ff
Data, preprocessing 104
Data, smoothing 22 57
Data, space 338
Data, understanding 336
Data-dependent representation 342ff
DE 2 variant of differential evolution 215 218ff
DE l variant of differential evolution 215 216ff
De-seasonalizing 21
De-trending 21
Decision boundary 94
Decision surface 94
Decision trees 87
Decomposition analysis 21
Defuzzification 146
Defuzzifier 146
Degree of belongingness 144
Degree of fulfilment 153
Delta learning rule 312
Delta rule 82 88
Dempster — Shafer theory 5
| Dendrites 81
Destructive evolving of NN 306
Determination of number of input nodes 106
Developmental rules 312
Differential evolution 197 215
Dilation coefficients 348
Dimensionality reduction 34 291
Diophantine equation 62 69
Direct encoding approach 307
Direct encoding strategies 309
Discrete affine wavelet transform 348
Disorderly configured data set 356
Dissimilar fuzzy sets 281
Distinguishable fuzzy sets 298
Diversity measure 323
Duplication 196
Dynamic learning rate 115
Dynamic recurrent networks 91
Dynamically controlled GAs 329
Early stopping 117 118 120
Edge encoding 308 312
EFC(T) see Entropy-based fuzzy clustering 355ff
Eigen-nodes 124
Elementary learning process 350
Elitist strategy 215
Elman network 88
Energy function 89
Enhanced transparency 277
Entropy measure for cluster estimation 356
Entropy-based fuzzy clustering 355ff 358
Error-correction learning 85
Estimation set 118
Evidence theory 6
Evolution of evolution 7
Evolution of evolution strategy 7
Evolution window 213
Evolutionary algorithms 196ff
Evolutionary computation 4 6ff 195 231
Evolutionary law 90
Evolutionary operators 195
Evolutionary programming 7 195 197 214ff
Evolutionary programming algorithm 214ff
Evolutionary strategies 7 195 197 212ff
Evolutionary systems 197
Evolving complete network 311
Evolving connection weights 306ff
Evolving fuzzy logic systems 313ff
Evolving network architecture 310ff
Evolving neural networks 305ff
Evolving the activation function 312
Excitatory neurons 352
Experiment design 112
Exploitation-to-exploration rate 323
Failure diagnosis 68
FAM see Fuzzy associative memory
Feature space 337
features 174
Feedforward networks 80
Feedforward neuro-fuzzy system 230
Final prediction error 123
Finite-state automata 7
Fitness 6 196
Fitness function 7 323
Fitness measure in genetic programming 211ff
Fitness windowing 201ff
Fixed-point attractor 88
Fixed-point learning 90
Forecasting, chaotic time series using fuzzy logic 169ff
Forecasting, methodology 49 103ff
Forecasting, multivariate time series 136
Forecasting, nonstationary processes 66
Forecasting, of electrical load 249
Forecasting, using adaptive smoothing 62
Forecasting, using Box — Jenkins method 53ff
Forecasting, using exponential smoothing 58
Forecasting, using fuzzy logic approach 169ff
Forecasting, using neural networks 129ff
Forecasting, using neuro-fuzzy system 230ff
Forecasting, using regression approaches 51ff
Forecasting, using simple moving average 57
Forecasting, using smoothing 57
Forecasting, using trend analysis 51
Four-layer network 88
Fourier series model 39
Fractally configured networks 350ff
Fractally configured neural networks 335 350ff
Frequency domain approach 18
Frequency domain models 39
Frontal association cortices 351
Full interconnection 111
Fully connected recurrent network 90 91
Function defining branches 211
Functional knowledge 336
Fuzzifier 146
Fuzziness 5 6
Fuzzy associative memory 226
Fuzzy c-means algorithm 179ff 352
Fuzzy C-means clustering 178ff
Fuzzy clustering 198 279 352
Fuzzy clustering algorithm 173ff
Fuzzy expert systems 146
Fuzzy government module 329
Fuzzy implication 151
Fuzzy inference 224 225
Fuzzy inference engine 146
Fuzzy inference system 147
Fuzzy input regions 159
Fuzzy knowledge 5
Fuzzy Kohonen clustering networks 353
Fuzzy logic 3 4 143
Fuzzy logic, approach 143ff
Fuzzy logic, systems 146ff
Fuzzy logic, technology 336
Fuzzy model identification using EFC 359
Fuzzy modelling 277ff
Fuzzy net controller 316
Fuzzy neuro systems 4
Fuzzy neurons 224 227ff
Fuzzy output regions 159
Fuzzy partition 177ff
Fuzzy probability 6
Fuzzy reasoning 5
Fuzzy rule base generation 157ff
Fuzzy rule systems 146
Fuzzy set 143
Fuzzy-logic-based neurons 224
Fuzzy-logic-controlled GAs 329ff
GA see Genetic algorithm
Gabor transform 345
Gauss — Newton method 103 240
Gauss — Newton modification 102
Gaussian function 86
Gbest solution 336
General predictive control 71
General systems theory 350
Generalization, attribute 112
Generalization, capability 125
Generalization, of Hausdorff distance 284
Generalized autoregressive operator 29
Generalized backpropagation rule 90
Generalized delta rule 95
Generalized likelihood ratio 48
Generalized optimal brain surgeon 124
Generalized RBF network 349
Genes 6
Genetic algorithm (GA) 7 195 197 231
Genetic Algorithm (GA), adaptation at component level 322
Genetic Algorithm (GA), adaptation at individual level 322
Genetic Algorithm (GA), adaptation at initial stage 324
Genetic Algorithm (GA), adaptation at population level 322
|
|
 |
Реклама |
 |
|
|