Multi-resolution Methods for Modeling and Control of Dynamical Systems

Multi-resolution Methods for Modeling and Control of Dynamical Systems

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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.show more

Product details

  • Hardback | 320 pages
  • 154 x 236 x 22mm | 580.6g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 120 black & white illustrations, 8 colour illustrations, 14 black & white tables
  • 1584887699
  • 9781584887690

About John L. Junkins

University at Buffalo, New York, USA Texas A&M University, College Station, USA University of Surrey, UKshow more

Table of contents

Least Square Methods The Least Square Algorithm Linear Least Square Methods Nonlinear Least Squares Algorithm Properties of Least Square Algorithms Examples Polynomial Approximation Gram-Schmidt Procedure of Orthogonalization Hypergeometric Function Approach to Generate Orthogonal Polynomials Discrete Variable Orthogonal Polynomials Approximation Properties of Orthogonal Polynomials Artificial Neural Networks for Input-Output Approximation Introduction Direction-Dependent Approach Directed Connectivity Graph Modified Minimal Resource Allocating Algorithm (MMRAN) Numerical Simulation Examples Multi-Resolution Approximation Methods Wavelets Bezier Spline Moving Least Squares Method Adaptive Multi-Resolution Algorithm Numerical Results Global-Local Orthogonal Polynomial MAPping (GLO-MAP) in N Dimensions Basic Ideas Approximation in 1, 2, and N Dimensions Using Weighting Functions Global-Local Orthogonal Approximation in 1-, 2-, and N-Dimensional Spaces Algorithm Implementation Properties of GLO-MAP Approximation Illustrative Engineering Applications Nonlinear System Identification Problem Statement and Background Novel System Identification Algorithm Nonlinear System Identification Algorithm Numerical Simulation Distributed Parameter Systems MLPG-Moving Least Squares Approach Partition of Unity Finite Element Method Control Distribution for Over-Actuated Systems Problem Statement and Background Control Distribution Functions Hierarchical Control Distribution Algorithm Numerical Results Appendix References Index Each chapter contains an Introduction and a Summary.show more