Multi-Resolution Methods for Modeling and Control of Dynamical Systems

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A01=John L. Junkins
A01=Puneet Singla
adaptive control theory
advanced mathematical modeling
approximation
approximations
artificial neural networks
Author_John L. Junkins
Author_Puneet Singla
basis
benchmark computational methods
Category=PHD
Control Distribution
Control Distribution Algorithm
Control Distribution Problem
Control Moment Gyros
Conventional Fem
distributed parameter analysis
Distribution Function Approach
Distribution Functions
dynamical systems
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
error
Feasible Solution Set
function
functions
Gaussian Basis Function
GLO-MAP
input-output approximation
least squares algorithm
local
Meshless Methods
MLSA
Multi-resolution Algorithms
multi-resolution methods
neural network system identification
nonlinear system modeling
orthogonal
Orthogonal Basis Functions
Orthogonal Polynomials
Over-actuated Systems
polynomial
Polynomial Basis Functions
polynomials
Puneet Singla
RBF
RBF Network
real-time learning algorithms
Sensitivity Matrix
Shape Functions
SJA
State Vector
Synthetic Jet
V1 V2 V3
Weight Function

Product details

  • ISBN 9781584887690
  • Weight: 588g
  • Dimensions: 156 x 234mm
  • Publication Date: 01 Aug 2008
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.

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

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