Cart (Loading....) | Create Account
Close category search window

Pattern sequence recognition using a time-varying Hopfield network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
1 Author(s)
Donq-Liang Lee ; Dept. of Electron. Eng., Ta-Hwa Inst. of Technol., Hsin-Chu, Taiwan

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

Published in:

Neural Networks, IEEE Transactions on  (Volume:13 ,  Issue: 2 )

Date of Publication:

Mar 2002

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.