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Feature-enhanced IPA neural network and optical realization

The dynamic behavior of a neural network is demonstrated by its interconnection weighted matrix. In this paper, we present a Feature Enhanced Interpattern Association (FEIPA) neural network model which is sensitive to special features of reference patterns in the reconstruction. We think of the common part of the stored patterns as redundance and discard its contributions in the associating process. It is equal to enhance the role of special features of the reference pattern in the IWM and in the reconstruction procession. Therefore the IWM of FEIPA is well-distributed and the output before threshold is a little more uniform than that of IPA model. A 2D (8 X 8) optical system is constructed using lenslet array as interconnection to realize the FEIPA model. Digital simulation and experiment results are provided.