Stochastic convergence analysis of a two-layer perceptron for a system identification model

Author

Bershad, Neil J. ; Cowan, Colin F N ; Shynk, John J.

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA

fYear

1991

fDate

4-6 Nov 1991

Firstpage

212

Abstract

The authors analyze the stationary points of a two-layer perceptron which attempts to identify the parameters of a specific nonlinear system. The training sequence is modeled as the binary output of the nonlinear system when the input is composed of an independent sequence of zero mean Gaussian vectors with independent components. The training rule is a modified version of Rosenblatt´s algorithm. It is shown that the two-layer perceptron correctly identifies all parameters of the unknown nonlinear system

Keywords

identification; neural nets; nonlinear systems; stochastic processes; binary output; nonlinear system; parameter identification; stationary points; stochastic convergence analysis; system identification model; training rule; training sequence; two-layer perceptron; zero mean Gaussian vectors; Backpropagation algorithms; Convergence; Councils; Hydrogen; Laboratories; Linear systems; Multilayer perceptrons; Nonlinear systems; Stochastic systems; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1991. 1991 Conference Record of the Twenty-Fifth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-2470-1

Type

conf

DOI

10.1109/ACSSC.1991.186443

Filename

186443