This book contains thirty timely contributions in the emerging field of Computational Intelligence (CI) with reference to system control design and applications. The three basic constituents ofCI are neural networks (NNs). fuzzy logic (FL) I fuzzy reasoning (FR). and genetic algorithms (GAs). NNs mimic the distributed functioning of the human brain and consist of many. rather simple. building elements (called artificial neurons) which are controlled by adaptive parameters and are able to incorporate via learning the knowledge provided by the environment, and thus respond intelligently to new stimuli. Fuzzy logic (FL) provides the means to build systems that can reason linguistically under uncertainty like the human experts (common sense reasoning). Both NNs and FL I FR are among the most widely used tools for modeling unknown systems with nonlinear behavior. FL suits better when there is some kind of knowledge about the system. such as, for example, the linguistic information of a human expert. On the other hand. NNs possess unique learning and generalization capabilities that allow the user to construct very accurate models of nonlinear systems simply using input-output data. GAs offer an interesting set of generic tools for systematic random search optimization following the mechanisms of natural genetics. In hybrid Computational Intelligence - based systems these three tools (NNs, FL, GAs) are combined in several synergetic ways producing integrated tools with enhanced learning, generalization. universal approximation. reasoning and optimization abilities.
The field of computational intelligence (CI) has expanded rapidly during recent years with a large and still increasing range of applications. This book contains a cohesive set of thirty contributions that cover a wide, well-selected range of topics. The book is organized in the following five parts: Part I: Neural network-based intelligent estimation and control Part II: Fuzzy logic-based intelligent estimation and control Part III: Genetic algorithm-based system analysis and design Part IV: Hybrid intelligent techniques in system analysis and control Part V: Computational intelligence in engineering applications Parts I-IV include general overviews, new techniques and practical CI algorithms for system analysis, identification, optimization and control, and Part V contains nine important applications of CI dealing with robot control, collision avoidance of dangerous ships, automated visual inspection, control of fed-batch bioprocesses, identification of naval turbochargers, semi-active suspension design of ground vehicles, ecological populations' modeling, vehicle navigation, state estimation of electric power systems, and multi-robot cooperative control. The book can be used as a reference volume by researchers and practitioners in the field, and as a rich unified information source by teachers and students in related postgraduate programs, thus saving considerable time in searching the scattered literature in the field.
Preface. Contributors. Part I: Neural Network-Based Intelligent Estimation and Control. 1. Intelligent Forecasting and Fault Diagnosis Using Neural Estimators; E.S. Tzafestas, S.G. Tzafestas. 2. Parameter Identification of Dynamical Systems Using Neural Networks; N. Yadaiah, et al. 3. Recent Developments in Neural Network PID Autotuning; A.E.B. Ruano. 4. Adaptive Self-Tuning Neural-Network-Based Controller; P. Potocnik, I. Grabec. 5. Neuro-Control for Multivariable Systems; S. Omatu, M. Yoshioka. Part II: Fussy Logic-Based Intelligent Estimation and Control. 6. Orthogonal Least Squares Based Fuzzy Model For Short Term Load Forecasting; P.A. Mastorokostas, et al. 7. An Unsupervised Fussy Classification Algorithm for Non Elliptic Classes; P. Billaudel, et al. 8. Necessary and Sufficient Conditions for Stability of Dynamic Fuzzy Systems; F. Matía, et al. 9. Fuzzy Behavior-Based Control with Local Learning; K. Izumi, K. Watanabe. 10. A Stable and Robust Fuzzy Controller for the Position Control of Robots; Z. Doulgeri, D. Biskas. 11. Stability Analysis and Fuzzy Self-Tuning Control of a Nonlinear Process with Limit Points; D.I. Sagias, et al. Part III: Genetic Algorithm-Based System Analysis and Design. 12. Application of the Genetic Algorithm (Approach) to a Cellular Dynamic Channel Allocation Model; H.G. Sandalidis, et al. 13. Applying Genetic Algorithms to Workshop Cell Decomposition; P. De Lit, et al. 14. A Genetic Algorithm for Efficient Video Content Representation; A.D. Doulamis, et al. 15. Fast Synthetic Genetic Algorithm Combined with BP &endash; Application to Short-Term Economic Dispatch of Hydrothermal Power Systems; X. Meng, et al. 16. Parallel Platform Design and Kinematics Analysis Using Evolution Strategy Via Regeneration; A.E. Kanarachos, K.S. Roussis. Part IV: Hybrid Intelligent Techniques in System Analysis and Control. 17. A Hybrid Neural-Classical Structure for the Modelling of a Bioprocess; A. Hanomolo, et al. 18. Estimation of State Variable in Power System Combining Theoretical Knowledge with Neural Network; T. Atanasova, et al. 19. Route Selection in a Neuro-Fuzzy Vehicle Navigation System; G.K.H. Pang. 20. Hierarchical Position Control Using Fuzzy Logic: Opportunities and Limitations; A. Zilouchian, et al. 21. Impedance Control for Multi-DOF Manipulator by Learning Environment Models; F. Nagata, et al. Part V: Computational Intelligence in Engineering Applications. 22. Engineering Applications of N-Step Ahead Neurocontrol; A. Hountras, et al. 23. Neural Classification of Dangerous Ships in Collision Situations; J. Lisowski, et al. 24. Neural Computing in Fabrics Inspection System; H. Liu, et al. 25. Control of a Fed-Batch Bioprocess by Using Neural Network Observers; Y.S. Boutalis, O.I. Kosmidou. 26. Neural Networks for the Model Identification of the Naval Turbochargers; N.G. Pentelelis, et al. 27. An Optimum Suspension System for Vehicles Using Fuzzy Reasoning; A. Kanarachos, et al. 28. Fuzzy Markov Systems for the Description of Population Dynamics; M.A. Symeonaki, et al. 29. Concerning Hopfield Networks: An Overview with Application to System Identification and Control; S.G. Tzafestas, D. Vogiatzis. 30. Cooperative Control Using Multiple Manipulators with different Command Inputs; K. Izumi, et al. Index.