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Proceedings Paper

Design and noniterative learning of multiple pattern storage in a modified Hopfield net
Author(s): Chia-Lun John Hu
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Paper Abstract

A Hopfield net is a feed-back neural network (FBNN) consisting of one layer of binary neurons. Its main function is its ability to associatively store multiple binary patterns (or accurate binary patterns) in its connection matrix, and to associatively recall on any of these stored patterns by a nosie affected triggering pattern applied to the input. If the noise of this input pattern falls within a certain range, (called the domain of convergence or the domain of attraction in the terminology of nonlinear system analysis), of a certain accurately stored pattern, then that accurate pattern will be recalled and will appear permanently in the output of the FBNN even when the triggering input pattern is removed. This being so is due to the self-sustained feedback action and the domain of convergence properties existing in the FBNN. However, if the stored patterns and the noisy, triggering patterns violate some PLI (positive, linear, independency) condition, then it is impossible for the FBNN to learn the mapping relations of these accurate patterns to be triggered by the triggering patterns with the designated noisy range. In this case we have to design a "UNIVERSAL" two-layered feed-back neural network that will accomplish this learning task. This paper dervies from the principle of the NONITERATIVE LEARNING, the design of a universal, parallel-cascaded two-layered perceptron (PCTLP), and the reconnection of it to form a universal FBNN, (which may be called the generalized Hopfield net,) that will accomplish this associative-storage and associative-recall learning task.

Paper Details

Date Published: 6 August 2003
PDF: 7 pages
Proc. SPIE 5106, Optical Pattern Recognition XIV, (6 August 2003); doi: 10.1117/12.501407
Show Author Affiliations
Chia-Lun John Hu, Southern Illinois Univ., Carbondale (United States)

Published in SPIE Proceedings Vol. 5106:
Optical Pattern Recognition XIV
David P. Casasent; Tien-Hsin Chao, Editor(s)

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