Fuzzy data recognition by polynomial bidirectional heteroassociator

This investigation presents a novel method of pattern recognition using the polynomial bidirectional heteroassociator (PBH). This network can be used for the industrial application of optical character recognition. According to detailed simulations, the PBH has a higher capacity for pattern pair storage than that of the conventional bidirectional associative memories and fuzzy memories. Meanwhile, the practical capacity of a PBH 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. The PBH takes advantage of fuzzy characteristics in evolution equations such that the signal-noise-ratio is significantly increased. We apply the result of this research to pattern recognition problems. The practical capacity of the fuzzy data recognition using the PBH considering fault tolerance in the worst case is also estimated. Simulation results are presented to verify the derived theory