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

N.J Bershad,C.F.N Cowan,J.J Shynk

doi:10.1109/acssc.1991.186443

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

N.J Bershad, C.F.N Cowan + Show 1 more

https://doi.org/10.1109/acssc.1991.186443

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Publication Date: Nov 4, 1991

Citations: 3

Affiliation: University of California, Irvine, Loughborough University, University of California, Santa Barbara

#Parameters Of Nonlinear System #Rosenblatt's Algorithm + Show 8 more

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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. >

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