All-optical multilayer perceptrons differ in various ways from the ide
al neural network model. Examples are the use of nonideal activation f
unctions, which are truncated, asymmetric, and have a nonstandard gain
; restriction of the network parameters to non-negative values, and th
e limited accuracy of the weights. A backpropagation-based learning ru
le is presented that compensates for these nonidealities and enables t
he implementation of all-optical multilayer perceptrons where learning
occurs under computer control. The good performance of this learning
rule, even when using a small number of weight levels, is illustrated
by a series of computer simulations incorporating the nonidealities. (
C) 1998 Society of Photo-Optical Instrumentation Engineers.