Muutke küpsiste eelistusi

Neural Adaptive Control Technology [Kõva köide]

Edited by (Daimler-benz Ag, Germany), Edited by (Cranfield Univ, Uk)
Teised raamatud teemal:
Neural Adaptive Control TechnologySuurem pilt
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789810225575.html
This book is an outgrowth of the workshop on Neural Adaptive Control Technology, NACT I, held in 1995 in Glasgow. Selected workshop participants were asked to substantially expand and revise their contributions to make them into full papers.The workshop was organised in connection with a three-year European Union funded Basic Research Project in the ESPRIT framework, called NACT, a collaboration between Daimler-Benz (Germany) and the University of Glasgow (Scotland). A major aim of the NACT project is to develop a systematic engineering procedure for designing neural controllers for nonlinear dynamic systems. The techniques developed are being evaluated on concrete industrial problems from Daimler-Benz.In the book emphasis is put on development of sound theory of neural adaptive control for nonlinear control systems, but firmly anchored in the engineering context of industrial practice. Therefore the contributors are both renowned academics and practitioners from major industrial users of neurocontrol.
Preface v Part I. Neural Adaptive Control Technology 3(112)
Chapter 1 Discrete-Time Neural Model Structures for Continuous Nonlinear Systems: Fundamental Properties and Control Aspects 3(38) J. C. Kalkkuhl K. J. Hunt (Daimler-Benz AG)
Chapter 2 Continuous-Time Local Model Networks 41(30) P. J. Gawthrop (University of Glasgow)
Chapter 3 Nonuniform Sampling Approach to Control Systems Modelling with Feedforward Neural Networks 71(44) R. Zbikowski A. Dzielinski (University of Glasgow) Part II. Nonlinear Control Fundamentals for Neural Networks 115(92)
Chapter 4 Geometric Methods in Nonlinear Control Theory: A Survey 115(38) W. Respondek (Polish Academy of Sciences)
Chapter 5 Local Reachability, Local Controllability and Observability of a Class of 2-D Bilinear Systems 153(24) T. Kaczorek (Warsaw University of Technology)
Chapter 6 Stable Adaptive Control of a General Class of Non-Linear Systems 177(30) T. A. Johansen (SINTEF) M. M. Polycarpou (University of Cincinnati) Part III. Neural Techniques and Applications 207(134)
Chapter 7 Robust Adaptive Neurocontrol of MIMO Continuous-Time Processess Based on the e1-Modification Scheme 207(30) J. -M. Renders M. Saerens (Universite Libre de Bruxelles)
Chapter 8 Black-Box Modeling with State-Space Neural Networks 237(28) I. Rivals L. Personnaz (Ecole Superieure de Physique et de Chimie Industrielles)
Chapter 9 An Approach to Intelligent Identification and Control of Nonlinear Dynamical Systems 265(20) D. A. Sofge D. L. Elliott (NeuroDyne, Inc.)
Chapter 10 How to Adapt in Neurocontrol: A Decision for CMAC 285(30) W. S. Mischo (Darmstadt Institute of Technology)
Chapter 11 The Equivalence of Spline Models and Fuzzy Logic Applied to Model Construction and Interpretation 315(26) G. T. Line T. Kavli (SINTEF Instrumentation) Index 341