Pattern Recognitin by High-Capacity Polynomial Bidirectional Hetero-Associative Network

This investigation presents a novel method of image processing using the polynomial bidirectional hetero-associative network (PBHAN). This network can be used for industrial application of optical character recognition. According to the results of detailed simulations, the PBHAN has a higher capacity for pattern pair storage than do the conventional bidirectional associative memories and fuzzy memories. The practical capacity of a PBHAN considering fault tolerance is discussed. The fault tolerance requirement leads to the discovery of the attraction radius of the basin for each stored pattern pair. PBHAN takes advantage of fuzzy characteristics in evolution equations such that the signal-noise-ratio is significantly increased. In this work, we apply the result of this research to pattern recognition problems. The practical capacity of fuzzy data recognition using PBHAN and considering fault tolerance in the worst case is also estimated. Simulation results are presented to verify the derived theory.