Analysis of a perceptron learning algorithm with momentum updating

Author

Shynk, John J. ; Roy, Sumit

Author_Institution

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

fYear

1990

fDate

3-6 Apr 1990

Firstpage

1377

Abstract

An analysis is presented of the stationary points of an adaptive algorithm that adjusts the perceptron weights. This algorithm is identical in form to the least-mean-square (LMS) algorithm, except that a hard limiter is incorporated at the output of the summer. In addition, a momentum term is included in the weight update; this modified algorithm is referred to as the momentum LMS (MLMS) algorithm. It is shown that the stationary points of the MLMS algorithm are not unique; they depend not only on the statistics of the input and the desired response, but also on the specific values used for the algorithm convergence parameters, the step size and convergence constant

Keywords

learning systems; least squares approximations; neural nets; MLMS algorithm; adaptive algorithm; hard limiter; momentum least mean squares algorithm; momentum updating; neural network; perceptron learning algorithm; stationary points; Adaptive algorithm; Algorithm design and analysis; Computer networks; Convergence; Feedforward neural networks; Feedforward systems; Least squares approximation; Multi-layer neural network; Multilayer perceptrons; Neural networks; Statistics; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.115643

Filename

115643