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Popovic D., Palit A.K. — Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications
Popovic D., Palit A.K. — Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications



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Название: Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications

Авторы: Popovic D., Palit A.K.

Аннотация:

Foresight in an engineering enterprise can make the difference between success and failure and can be vital to the effective control of industrial systems. Forecasting the future from accumulated historical data is a tried and tested method in areas such as engineering finance. Applying time series analysis in the on-line milieu of most industrial plants has been more problematic because of the time and computational effort required. The advent of soft computing tools such as the neural network and the genetic algorithm offers a solution.

Chapter by chapter, Computational Intelligence in Time Series Forecasting harnesses the power of intelligent technologies individually and in combination. Examples of the particular systems and processes susceptible to each technique are investigated, cultivating a comprehensive exposition of the improvements on offer in quality, model building and predictive control, and the selection of appropriate tools from the plethora available; these include:

• forecasting electrical load, chemical reactor behaviour and high-speed-network congestion using fuzzy logic;

• prediction of airline passenger patterns and of output data for nonlinear plant with combination neuro-fuzzy networks;

• evolutionary modelling and anticipation of stock performance by the use of genetic algorithms.

Application-oriented engineers in process control, manufacturing, the production industries and research centres will find much to interest them in Computational Intelligence in Time Series Forecasting and the book is suitable for industrial training purposes. It will also serve as valuable reference material for experimental researchers


Язык: en

Рубрика: Технология/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Издание: 1st edition

Год издания: 2005

Количество страниц: 396

Добавлена в каталог: 11.12.2007

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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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
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