Feature-enhanced IPA neural network and optical realization

Wenlu Wang; Minxian Wu; Shutian Liu; Jie Wu; Chunfei Li

doi:10.1117/12.163593

9 November 1993 Feature-enhanced IPA neural network and optical realization

Wenlu Wang, Minxian Wu, Shutian Liu, Jie Wu, Chunfei Li

Proceedings Volume 2026, Photonics for Processors, Neural Networks, and Memories; (1993) https://doi.org/10.1117/12.163593
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

Abstract

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.

Citation Download Citation

Wenlu Wang, Minxian Wu, Shutian Liu, Jie Wu, and Chunfei Li "Feature-enhanced IPA neural network and optical realization", Proc. SPIE 2026, Photonics for Processors, Neural Networks, and Memories, (9 November 1993); https://doi.org/10.1117/12.163593

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