Discrete all-positive multilayer perceptrons for optical implementation

Abstract

Abstract. All-optical multilayer perceptrons di er in various ways from the ideal neural network model. Examples are the use of non-ideal activation functions which are truncated, asymmetric, and have a non-standard gain, restriction of the network parameters to non-negative v alues, and the limited accuracy of the weights. In this paper, a backpropagation-based learning rule is presented that compensates for these non-idealities and enables the implementation of all-optical multilayer perceptrons where learning occurs under control of a computer. The good performance of this learning rule, even when using a small number of weight l e v els, is illustrated by a series of computer simulations incorporating the non-idealities. IDIAP{RR 97-02