Title :
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
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;
Conference_Titel :
Signals, Systems and Computers, 1991. 1991 Conference Record of the Twenty-Fifth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-2470-1
DOI :
10.1109/ACSSC.1991.186443
