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Full Description
Unifying the most important methodology in this field, Multi-Resolution Methods for Modeling and Control of Dynamical Systems explores existing approximation methods as well as develops new ones for the approximate solution of large-scale dynamical system problems. It brings together a wide set of material from classical orthogonal function approximation, neural network input-output approximation, finite element methods for distributed parameter systems, and various approximation methods employed in adaptive control and learning theory.
With sufficient rigor and generality, the book promotes a qualitative understanding of the development of key ideas. It facilitates a deep appreciation of the important nuances and restrictions implicit in the algorithms that affect the validity of the results produced. The text features benchmark problems throughout to offer insights and illustrate some of the computational implications. The authors provide a framework for understanding the advantages, drawbacks, and application areas of existing and new algorithms for input-output approximation. They also present novel adaptive learning algorithms that can be adjusted in real time to the various parameters of unknown mathematical models.
Contents
Least Square Methods. Polynomial Approximation. Artificial Neural Networks for Input-Output Approximation. Multi-Resolution Approximation Methods. Global-Local Orthogonal Polynomial MAPping (GLO-MAP) in N Dimensions. Nonlinear System Identification. Distributed Parameter Systems. Control Distribution for over Actuated Systems. Appendix. References. Index.
