Class of learning algorithms for multilayer perceptron

M. Abbasi; Mohammed R. Sayeh

doi:10.1117/12.47758

1 March 1991 Class of learning algorithms for multilayer perceptron

M. Abbasi, Mohammed R. Sayeh

Proceedings Volume 1396, Applications of Optical Engineering: Proceedings of OE/Midwest '90; (1991) https://doi.org/10.1117/12.47758
Event: Applications of Optical Engineering: Proceedings of OE/Midwest '90, 1990, Rosemont, IL, United States

Abstract

A class of learning techniques for neural networks can be considered as optimization problems. The connection strengths are modified such that the difference between the network response and a desired response Is minimized. In this paper the learning techniques based on the gradient momentum Newton and quasi-Newton methods are considered. A learning algorithm is also developed based on the conjugate gradient technique. These learning techniques are applied to the Exclusive-OR problem for comparison of their performance. For this problem the algorithm based on the conjugate gradient technique converges faster than the other algorithms. 2.

Citation Download Citation

M. Abbasi and Mohammed R. Sayeh "Class of learning algorithms for multilayer perceptron", Proc. SPIE 1396, Applications of Optical Engineering: Proceedings of OE/Midwest '90, (1 March 1991); https://doi.org/10.1117/12.47758

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