New bidirectional neural network and application to binary image recognition

Shengwei Zhang; Anthony G. Constantinides; Lihe Zou

doi:10.1117/12.24104

1 September 1990 New bidirectional neural network and application to binary image recognition

Shengwei Zhang, Anthony G. Constantinides, Lihe Zou

Proceedings Volume 1360, Visual Communications and Image Processing '90: Fifth in a Series; (1990) https://doi.org/10.1117/12.24104
Event: Visual Communications and Image Processing '90, 1990, Lausanne, Switzerland

Abstract

A new bidirectional neural network is proposed based on convex projection theory. The neurons in the network are divided into two classes, clamped and floating neurons. For clamped neurons their states are preassigned to some fixed values and provide the network stimulus. For floating neurons their states change in accordance to those of other neurons and provide the network response. Steady state solutions under synchronous operation are presented in a closed-form formula. An adaptive learning algorithm is discussed which does not need matrix inverse computation and thus saves much learning time. Experiments in storing and retrieving binary images are carried out on a data base composed of 26 uppercase and lower-case English characters.

Citation Download Citation

Shengwei Zhang, Anthony G. Constantinides, and Lihe Zou "New bidirectional neural network and application to binary image recognition", Proc. SPIE 1360, Visual Communications and Image Processing '90: Fifth in a Series, (1 September 1990); https://doi.org/10.1117/12.24104

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