Approximation Capability of a Novel Neural Network Model for Dynamic Systems

Author

Zhang, Jianhai ; Kong, Wanzeng ; Zhang, Senlin ; Liu, Meiqin

Author_Institution

Coll. of Comput., Hangzhou Dianzi Univ., Hangzhou, China

Volume

1

fYear

2009

Firstpage

59

Lastpage

62

Abstract

The approximation power for dynamic systems of a novel neural network model-standard neural network model (SNNM) is examined. Applying Stone-Weierstrass theorem, it is proved that SNNM is capable of approximating dynamic systems to any degree of accuracy. Furthermore, the results are briefly extended for any bounded measurable functions. The approximation capability together with the learn ability justify the use of SNNM in practical applications.

Keywords

approximation theory; neural nets; Stone-Weierstrass theorem; approximation capability; approximation power; dynamic systems; measurable function; standard neural network model; Automation; Cellular neural networks; Computer networks; Educational institutions; Fuzzy control; Intelligent networks; Neural networks; Power system modeling; Recurrent neural networks; Stability analysis; approximation capability; dynamic systems; recurrent neural network; standard neural network model;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on

Print_ISBN

978-0-7695-3804-4

Type

conf

DOI

10.1109/ICICTA.2009.23

Filename

5287709