Simplified learning algorithms for two-layer neural networks

Eduard Avedyan; Andrey Kerbelev; Ilya Levin; Yakov Tsypkin

doi:10.1117/12.195589

7 December 1994 Simplified learning algorithms for two-layer neural networks

Eduard Avedyan, Andrey Kerbelev, Ilya Levin, Yakov Tsypkin

Proceedings Volume 2430, Optical Memory & Neural Networks '94: Optical Neural Networks; (1994) https://doi.org/10.1117/12.195589
Event: Optical Memory and Neural Networks: International Conference, 1994, Moscow, Russian Federation

Abstract

Multilayer neural networks are widely applied in fields of pattern recognition, speech processing, optimization problems, non-linear identification, non-linear adaptive control and other applications. They are trained usually by the error back-propagation algorithm. The main calculation problem of the algorithm is the goal function gradient searching implemented successively backward from the output layer. Two-layer neural networks can solve the approximation problem for a complicated non-linear function of many variables, as well as be effectively applied to automatic control problems, namely for the non-linear dynamic object identification. Calculation of the goal function gradient can be performed directly for two- layer neural networks, omitting the error back-propagation procedure, while a large number of calculations on each step remain. A training procedure simplified from the calculation point of view aimed at hardware implementation is suggested below for two-layer neural networks.

Citation Download Citation

Eduard Avedyan, Andrey Kerbelev, Ilya Levin, and Yakov Tsypkin "Simplified learning algorithms for two-layer neural networks", Proc. SPIE 2430, Optical Memory & Neural Networks '94: Optical Neural Networks, (7 December 1994); https://doi.org/10.1117/12.195589

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