Adaptive Control & Estimation for Nonlinear Systems - Neural & Fuzzy Approximation Techniques (Obook)
Published on 28. May 2002
Software
Other digital
568 pages
978-0-471-22113-5 (ISBN)
Description
This title includes a solution manual for problems, provides MATLAB code for examples and solutions, and deals with robust systems in both theory and practice.
Reviews / Votes
" well organized very useful for a graduate level control or intelligent systems course " (International Journal of Robust and Nonlinear Control, January 2005) the text is well organised with topics judiciously selected to build on each other the discussion and motivations are rigorous (International Journal of Robust & Nonlinear Control, Vol.15, No.1, 10th January 2005) "...this is an excellent book. It is pedagogically sound and, hence, suitable as a text for graduate courses... I recommend it also as a very valuable resource to practitioners..." (International Journal of General Systems, Vol. 32, 2003)More details
Language
English
Place of publication
New York
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Weight
10 gr
ISBN-13
978-0-471-22113-5 (9780471221135)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Jeffrey T. Spooner | Manfredi Maggiore | Raúl Ordóñez
Stable Adaptive Control and Estimation for Nonlinear Systems
Neural and Fuzzy Approximator Techniques
E-Book
03/2004
Wiley
€159.99
Available for download
Person
JEFFREY T. SPOONER is a senior member of the technical staff at Sandia National Laboratories, Albuquerque, New Mexico. MANFREDI MAGGIORE is an assistant professor in the Department of Electrical and Computer Engineering at the University of Toronto, Canada. RAUL ORDO?EZ is an assistant professor in the Department of Electrical and Computer Engineering at the University of Dayton, Ohio. KEVIN M. PASSINO is a professor in the Department of Electrical Engineering at The Ohio State University.
Content
Introduction. PART I: FOUNDATIONS. Mathematical Foundations. Neural Networks and Fuzzy Systems. Optimization for Training Approximators. Function Approximation. PART II: STATE FEEDBACK CONTROL. Control of Nonlinear Systems. Direct Adaptive Control. Indirect Adaptive Control. Implementations and Comparative Studies. PART III:OUTPUT FEEDBACK CONTROL. Output Feedback Control. Adaptive Output Feedback Control. Applications. PART IV: EXTENSIONS. Discrete Time Systems. Decentralized Systems. Perspectives on Intelligent Adaptive Systems. For Further Study. Bibliography. Index.