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Computational Intelligence in Systems and Control Design and Applications

'Intelligent Systems, Control and Automation: Science and E…
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Titel: Computational Intelligence in Systems and Control Design and Applications

ISBN: 1402003943
EAN: 9781402003943
'Intelligent Systems, Control and Automation: Science and Engineering'.
Softcover reprint of the original 1st ed. 1999.
Book.
Sprache: Englisch.
Herausgegeben von S. G. Tzafestas
Springer Netherlands

30. November 2001 - kartoniert - 376 Seiten

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.
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 VideoContent 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 Marko
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