Iterative Learning Neurocomputing

Author

Sun, Mingxuan

Author_Institution

Coll. of Inf. Eng., Zhejiang Univ. of Technol., Hangzhou, China

fYear

2009

fDate

28-29 Dec. 2009

Firstpage

158

Lastpage

161

Abstract

This paper presents a neural network framework for implementing unknown time-varying mappings. A unified architecture of time-varying neural networks is proposed, and the methodology of iterative learning is used for the network training. Convergence results of the iterative learning least squares algorithm are derived under assumption of bounded input signals. Periodic neural networks are explored as well to be used as periodic function approximation tools.

Keywords

iterative methods; learning systems; least squares approximations; neural nets; iterative learning least squares algorithm; iterative learning neurocomputing; periodic function approximation tools; periodic neural networks; time-varying neural networks; unknown time-varying mappings; Artificial neural networks; Feedforward neural networks; Function approximation; Iterative algorithms; Iterative methods; Least squares approximation; Least squares methods; Neural networks; Neurons; Time varying systems; Neural networks; iterative learning; periodic systems; time-varying systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Wireless Networks and Information Systems, 2009. WNIS '09. International Conference on

Conference_Location

Shanghai

Print_ISBN

978-0-7695-3901-0

Electronic_ISBN

978-1-4244-5400-6

Type

conf

DOI

10.1109/WNIS.2009.47

Filename

5381874