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Pattern sequence recognition using a time-varying Hopfield network

This paper presents a novel continuous-time Hopfield-type network which is effective for temporal sequence recognition. The fundamental problem of recalling pattern sequences by neural networks is first reviewed. Since it is difficult to implement a desired flow vector field distribution by using conventional matrix encoding scheme, a time-varying Hopfield model (TVHM) is proposed. The weight matrix of the TVHM is constructed in such a way that its auto-correlation and cross-correlation parts are encoded from two different sets of patterns. With this mechanism, flow vectors between any two adjacent stored patterns are of the same directions. Moreover, the flow vector field distribution around a stored pattern can be modulated by the time variable. Then, theoretical results regarding the radii of attraction and the recalling dynamics of the TVHM are presented. The proposed approach is different from the existing methods because neither synchronous dynamics nor interpolated training patterns are required. A way of increasing the storage capacity of the TVHM is proposed. Finally, experimental results are presented to illustrate the validity, capacity, recall capability, and the applications of the proposed model.