DocumentCode
1708458
Title
Feasibility of random basis function approximators for modeling and control
Author
Tyukin, Ivan Yu ; Prokhorov, Danil V.
Author_Institution
Dept. of Math., Univ. of Leicester, Leicester, UK
fYear
2009
Firstpage
1391
Lastpage
1396
Abstract
We discuss the role of random basis function approximators in modeling and control. We analyze the published work on random basis function approximators and demonstrate that their favorable error rate of convergence O(1/n) is guaranteed only with very substantial computational resources. We also discuss implications of our analysis for applications of neural networks in modeling and control.
Keywords
computational complexity; convergence of numerical methods; function approximation; large-scale systems; computational resources; convergence; favorable error rate; neural network; random basis function approximator; Approximation error; Control system synthesis; Convergence; Error analysis; Gaussian processes; Intelligent control; Intelligent systems; Mathematical model; Neural networks; Radial basis function networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Applications, (CCA) & Intelligent Control, (ISIC), 2009 IEEE
Conference_Location
St. Petersburg
Print_ISBN
978-1-4244-4601-8
Electronic_ISBN
978-1-4244-4602-5
Type
conf
DOI
10.1109/CCA.2009.5281061
Filename
5281061
Link To Document