Optimal matching by the transiently chaotic neural network

Dynamic programming matching (DPM) is a technique that finds an optimal match between two sequences of feature vectors allowing for stretched and compressed sections of the sequence. The purpose of this study is to formulate the matching problem as an optimization task and carry out this optimization problem by means of a chaotic neural network. The proposed method uses TCNN, a Hopfield neural network with decaying self-feedback, to find the best-matching (i.e., the lowest global distance) path between an input and a template. Experimental results show a very good performance for the proposed algorithm in pattern recognition tasks.