Advances in Computational Intelligence: Theory And Applications (Series in Intelligent Control and Intelligent Automation)

Computational Intelligence (CI) is a recently emerging area in fundamental and applied research, exploiting a number of advanced information processing technologies that mainly embody neural networks, fuzzy logic and evolutionary computation. With a major concern to exploiting the tolerance for imperfection, uncertainty, and partial truth to achieve tractability, robustness and low solution cost, it becomes evident that composing methods of CI should be working concurrently rather than separately. It is this conviction that research on the synergism of CI paradigms has experienced significant growth in the last decade with some areas nearing maturity while many others remaining unresolved. This book systematically summarizes the latest findings and sheds light on the respective fields that might lead to future breakthroughs. Contents......Page 20 Preface......Page 6 List of Contributors......Page 14 1.1 Introduction......Page 25 1.2 Granular Computing......Page 26 1.3 Granular Computing and Logic: Synergistic Links......Page 29 1.4 Main Categories of Fuzzy Logic Processing Units......Page 31 1.5 A General Topology of the Network......Page 39 1.6 Interpretation Issues of Logic Networks......Page 41 Bibliography......Page 43 2.1 Introduction......Page 47 2.2 Type-I Linguistic Dynamic Systems......Page 50 2.3 Type-II Linguistic Dynamic Systems......Page 57 2.4 Linguistic Control Design for Goal States Specified in Words......Page 65 2.5 Conclusions......Page 74 Bibliography......Page 75 3 Slicing: A Distributed Learning Approach......Page 79 3.1 Introduction......Page 80 3.2 Slicing......Page 82 3.3 Variance Reduction in Slicing......Page 84 3.4 Experiments......Page 89 3.5 Analysis......Page 111 3.6 Discussion......Page 114 3.7 Conclusions......Page 116 Bibliography......Page 118 4 Marginal Learning Algorithms in Statistical Machine Learning......Page 123 4.1 Introduction......Page 124 4.2 Classification Problems and Margin......Page 126 4.3 Maximal Margin Algorithm in SVM......Page 127 4.4 Unbalanced Classification Problems and Weighted Maximal Margin Algorithms......Page 131 4.5 The n-Unsupervised Learning Problems and Margin......Page 145 4.6 Some Marginal Algorithms for One-Class Problems......Page 151 4.7 Some New Algorithms of Clustering Problems......Page 155 4.8 New Marginal Algorithms for PCA......Page 158 Bibliography......Page 163 5.1 Introduction......Page 169 5.2 Literature Survey......Page 172 5.3 Proposed Constraint Handling Scheme......Page 177 5.4 Constrained Optimization - Algorithm Design......Page 180 5.5 Selection Scheme Comparison Using TCG-2......Page 183 5.6 Test Results......Page 186 5.7 Conclusions......Page 190 Bibliography......Page 191 6.1 Introduction......Page 195 6.2 PSO EA and the Hybrid Algorithm......Page 197 6.3 Feedforward Neural Networks as Board Evaluator for the Game Capture Go......Page 203 6.4 Recurrent Neural Networks for Time Series Prediction......Page 219 6.5 Conclusions......Page 232 Bibliography......Page 233 7.1 Introduction......Page 239 7.2 Wavelet Networks......Page 241 7.3 Modular Structure of Wavelet-Fuzzy Networks......Page 242 7.4 Learning Algorithm for Wavelet-Fuzzy Networks......Page 254 7.5 Simulation Results......Page 263 Bibliography......Page 269 8.1 Introduction......Page 273 8.2 Principles of ACO......Page 274 8.3 Ant Colony Optimization......Page 275 8.4 Applications of ACO Algorithms......Page 277 Bibliography......Page 285 9 Motif Discoveries in DNA and Protein Sequences Using Self-Organizing Neural Networks......Page 295 9.1 Introduction......Page 296 9.2 Subsequences and Encoding......Page 300 9.3 Self-Organizing Neural Networks for Motif Identification......Page 305 9.4 Simulation Results......Page 314 9.5 Conclusions......Page 321 Bibliography......Page 322 10.1 Introduction......Page 327 10.2 Motivations......Page 328 10.3 Algorithms and Computational Complexities......Page 335 Bibliography......Page 338 11 Advances in Fingerprint Recognition Algorithms with Application......Page 341 11.1 Introduction......Page 342 11.2 Advances in Fingerprint Recognition Algorithms......Page 344 11.3 Application to Fingerprint Mobile Phone......Page 356 11.4 Conclusions......Page 362 Bibliography......Page 363 12.1 Introduction......Page 371 12.2 Control of Postural Stability......Page 373 12.3 Adaptive Control Strategy in Arm Reaching Movement......Page 391 Bibliography......Page 400 13.1 Introduction......Page 405 13.2 Problem Formulation......Page 407 13.3 Adaptive Backstepping-Based Design......Page 410 13.4 Adaptive Bounding Methods......Page 417 13.5 Supplementary Information......Page 427 13.6 Numerical Example......Page 429 13.7 Conclusions......Page 433 Bibliography......Page 434 14.1 Introduction......Page 437 14.2 SIRMs Dynamically Connected Fuzzy Inference Model......Page 439 14.3 Backing-Up Control of Truck-Trailer System......Page 442 14.4 Stabilization Control of Ball-Beam System......Page 450 14.5 Stabilization Control of Parallel-Type Double Inverted Pendulum System......Page 457 14.6 Conclusions......Page 470 Bibliography......Page 471 Index......Page 475