Abstract
An algorithm for optimizing a bipolar interconnection weight matrix
with the Hopfield network is proposed. The effectiveness of this
algorithm is demonstrated by computer simulation and optical
implementation. In the optical implementation of the neural network
the interconnection weights are biased to yield a nonnegative weight
matrix. Moreover, a threshold subchannel is added so that the
system can realize, in real time, the bipolar weighted summation in a
single channel. Preliminary experimental results obtained from the
applications in associative memories and multitarget classification
with rotation invariance are shown.
© 1999 Optical Society of America
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