Practical Applications of Computational Intelligence Techniques

By: Lakhmi Jain, Philippe De Wilde, L. C. Jain

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Hardcover | 30 April 2001

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404 Pages

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Computational intelligence paradigms have attracted the growing interest of researchers, scientists, engineers and application engineers in a number of everyday applications. These applications are not limited to any particular field and include engineering, business, banking and consumer electronics. Computational intelligence paradigms include artificial intelligence, artificial neural networks, fuzzy systems and evolutionary computing. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for optimisation applications, and fuzzy logic provides a basis for representing uncertain and imprecise knowledge.
Practical Applications of Computational Intelligence Techniques contains twelve chapters providing actual application of these techniques in the real world. Such examples include, but are not limited to, intelligent household appliances, aerial spray models, industrial applications and medical diagnostics and practice. This book will be useful to researchers, practicing engineers/scientists and students, who are interested in developing practical applications in a computational intelligence environment.

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Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Engineering & Technology Technology in General Engineering in General Mathematics Mathematical Foundation Mathematical Logic

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Paperback

Published: 14th September 2012

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An introduction to computational intelligence paradigms
Computational intelligence--a formal definition	p. 1
The logic of fuzzy sets	p. 2
Computational models of neural nets	p. 13
The back-propagation learning algorithm	p. 15
Hopfield nets	p. 20
Binary Hopfield net	p. 20
Continuous Hopfield net	p. 22
Self-organizing feature map	p. 22
Reinforcement learning	p. 24
Temporal difference learning	p. 26
Active learning	p. 27
Q-learning	p. 27
Genetic algorithms	p. 27
Deterministic explanation of Holland's observation	p. 31
Stochastic explanation of GA	p. 32
The fundamental theorem of genetic algorithms (schema theorem)	p. 32
The Markov model for convergence analysis	p. 34
Belief networks	p. 38
Computational learning theory	p. 45
Synergism of the computational intelligence paradigms	p. 47
Neuro-fuzzy synergism	p. 47
Weakly coupled neuro-fuzzy systems	p. 48
Tightly coupled neuro-fuzzy systems	p. 49
Fuzzy-GA synergism	p. 51
Neuro-GA synergism	p. 52
Adaptation of a neural learning algorithm using GA	p. 52
GA-belief network synergism	p. 54
Conclusions and future directions	p. 55
References	p. 57
Networked virtual park
Introduction	p. 65
The attraction builder	p. 66
Introduction	p. 66
Virtual avatars	p. 67
Avatar realism	p. 68
Face animation	p. 69
Body animation	p. 70
Speech animation	p. 70
The scene	p. 71
Adding interactivity	p. 71
Possible attractions	p. 75
Networked virtual environment system	p. 76
Introduction	p. 76
Overview of system architecture	p. 77
The client	p. 77
Introduction	p. 77
System communication	p. 78
Scene management	p. 79
Avatar representation and animation	p. 80
Navigation	p. 81
Audio communication	p. 82
Speech	p. 82
Devices	p. 82
Network manager	p. 83
The server	p. 84
Server overview	p. 84
Server database	p. 85
Client-server communication protocol	p. 86
Conclusion	p. 86
Acknowledgements	p. 87
References	p. 87
Commercial coin recognisers using neural and fuzzy techniques
Introduction	p. 89
Problem statement	p. 90
Problem analysis and database compilation	p. 92
Problem analysis	p. 93
Database compilation	p. 95
Optical measurements preprocessing	p. 96
Approach using artificial neural networks models	p. 98
Neural model selection	p. 99
Validation stage for the rejection of outliers	p. 104
Implementation	p. 107
Approach using fuzzy logic models	p. 113
Fuzzy model selection and experimental results	p. 114
Implementation	p. 117
Conclusions	p. 117
References	p. 119
Fuzzy techniques in intelligent household appliances
Introduction	p. 121
Fuzzy approaches for intelligent devices	p. 122
Introducing fuzziness to kitchen oven	p. 125
Thermostatically controlled oven	p. 126
Design of a fuzzy controller	p. 126
Results of fuzzy control	p. 130
Refrigerator-freezer control using fuzzy logic	p. 133
Refrigerating operating regime	p. 134
Fuzzy controller for the refrigerating device	p. 135
Results of fuzzy control of refrigerating device	p. 136
Results of fuzzy control of freezing device	p. 136
Measuring entire appliance within standard test environment	p. 137
Model and simulation of refrigerating-freezing appliance using one compressor	p. 138
Simulation results	p. 139
Hardware implementation	p. 141
Decrease of energy consumption from national point-of-view	p. 142
Conclusion	p. 143
References	p. 144
Neural prediction in industry: increasing reliability through use of confidence measures and model combination
Introduction	p. 147
Paper curl prediction	p. 149
Data collection	p. 150
Neural network model development	p. 151
Preprocessing	p. 152
Training	p. 154
Model combination	p. 155
Cranking	p. 156
Confidence measures	p. 158
Results	p. 162
In-specification/out-of-specification classifier	p. 162
Curl prediction	p. 163
Model combination	p. 164
Confidence measures	p. 167
Discussion	p. 169
Acknowledgments	p. 170
References	p. 170
Handling the back calculation problem in aerial spray models using a genetic algorithm
Introduction	p. 178
Early spray models	p. 179
FSCBG	p. 179
AGDISP	p. 180
AgDRIFT	p. 182
Computer simulation models in common	p. 183
Genetic algorithms	p. 185
Main GA components and how the GA works	p. 185
Sample GA applications	p. 188
Development of Fortran-SAGA	p. 188
AGDISP DOS version 7.0	p. 189
The Fortran GA	p. 190
Preliminary Fortran-based SAGA	p. 190
Results and discussion of Fortran-based SAGA	p. 191
Development of VB-SAGA 1.0	p. 196
VB-SAGA 1.0	p. 196
Exhaustive search test	p. 202
VB-SAGA1.0 test	p. 204
VB-SAGA1.0 experiments and results	p. 205
Development of VB-SAGA 2.0	p. 209
VB-SAGA2.0 menu items	p. 209
The self-adaptive GA	p. 211
Fuzzy logic control	p. 212
Development of self-adaptive GA in VB-SAGA2.0	p. 212
Results of VB-SAGA2.0	p. 215
Summary and conclusions	p. 217
References	p. 219
Genetic algorithm optimization of a filament winding process modeled in WITNESS
Introduction	p. 223
Filament winding model	p. 225
Genetic algorithm interface	p. 229
Results	p. 233
Conclusions	p. 236
Summary	p. 238
Acknowledgments	p. 238
References	p. 238
Genetic algorithm for optimizing the gust loads for predicting aircraft loads and dynamic response
Introduction	p. 241
Problem statement and related mathematical underpinnings	p. 244
Statistical discrete gust (SDG) model	p. 245
Methodology of the search for a worst-case gust	p. 245
Waveform construction	p. 246
Modified von Karman gust pre-filter	p. 248
Aircraft model simulation	p. 249
Linear aircraft model	p. 250
An approach using genetic algorithm	p. 253
Results of approach on linear aircraft model	p. 256
Wing bending moment	p. 256
Engine lateral acceleration	p. 257
Wing torque	p. 260
Aircraft normal acceleration	p. 263
Summary and conclusion	p. 265
Acknowledgments	p. 266
References	p. 266
A stochastic dynamic programming technique for property market timing
Introduction	p. 269
Review of theoretical considerations	p. 271
Specification of market timing model	p. 274
Stochastic dynamic programming	p. 282
Data used in the simulation study	p. 286
Performance and evaluation tests	p. 286
Performance of market timing strategy	p. 289
Comparison for various investment horizons	p. 290
Comparison of efficiency ratios	p. 292
Comparison for various transaction expenses	p. 292
Comparison for various cash downpayments	p. 293
Conclusions	p. 295
References	p. 296
A hybrid approach to breast cancer diagnosis
Introduction	p. 300
KBANNs	p. 301
KBANN methodology	p. 303
"Rules-to-network"	p. 303
Empirical module	p. 305
Overview of KBANN features	p. 305
Metabolic features of cancerous breast tissues	p. 306
Knowledge elicitation and refinement	p. 309
31P MRS data	p. 311
KBANN topology	p. 313
Results	p. 314
Knowledge-free networks	p. 314
KBANNs	p. 314
Discussion	p. 318
Analysis of final connection weights	p. 321
Conclusions	p. 324
Acknowledgements	p. 326
References	p. 326
Artificial neural networks as a computer aid for lung disease detection and classification in ventilation-perfusion lung scans
Introduction	p. 331
Artificial neural networks	p. 333
Materials and methods	p. 335
Overview of the AI diagnostic scheme	p. 335
Patient data	p. 336
Multifractal texture analysis	p. 339
Artificial neural network predictions	p. 340
Performance evaluation	p. 341
Results	p. 342
Analysis of perfusion lung scans	p. 342
Detection of lung disease	p. 342
Detection of pulmonary embolism	p. 343
Classification of lung diseases	p. 345
Analysis of perfusion-ventilation lung scans	p. 346
Detection of pulmonary embolism	p. 346
Classification of lung diseases	p. 347
Discussion	p. 348
Acknowledgments	p. 349
References	p. 350
Neural network for classification of focal liver lesions in ultrasound images
Introduction	p. 355
Texture analysis of focal liver lesions by neural networks	p. 358
Wavelet packets	p. 359
Multiscale texture features	p. 360
Optimal selection of multiscale texture features	p. 361
Combination of multiscale features by neural network	p. 363
Experimental results	p. 365
Database of focal liver lesions	p. 365
Experimental conditions	p. 366
Distribution of feature values	p. 367
Classification performance	p. 369
Performance of optimally selected multiscale features	p. 369
Performance of wavelet texture features and single-scale texture features	p. 370
Efficiency of multiscale texture features	p. 370
Performance of multiscale texture features in the distinction between different types of lesions	p. 371
Discussion	p. 373
Conclusions	p. 374
Acknowledgment	p. 374
Appendix	p. 375
Entropy	p. 375
Root mean square (RMS) variation	p. 375
First moment of power spectrum	p. 375
References	p. 376
Index	p. 379
Table of Contents provided by Syndetics. All Rights Reserved.

Practical Applications of Computational Intelligence Techniques

At a Glance

Hardcover

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How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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