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New bidirectional neural network and application to binary image recognition

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.