Date of Award

Fall 2002

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical & Computer Engineering

Program/Concentration

Computer Engineering

Committee Director

Vijayan K. Asari

Committee Member

Stephen A. Zahorian

Committee Member

Lee A. Belfore II

Call Number for Print

Special Collections LD4331.E55 S46 2002

Abstract

In this thesis, modular architectures and neighborhood-distance based learning algorithms for fast and effective convergence with increased storage capacity of Hopfield neural networks are presented. The main objective of this research work is to better understand the function of recurrent neural networks and the influence of modularity within a network, and to design, implement, and test the performance of modular Hopfield neural networks for pattern association. Mathematical analysis and results are provided to show that the speed, storage capacity, and generalization capability of the recurrent networks are improved significantly by incorporating the modular architectures and learning algorithms. A new ratio based learning rule for the hopfield network suitable for gray level image association without having the constraints of pattern orthogonality is also presented. Several experiments have been conducted to verify the performance of the proposed architectures and algorithms in terms of their convergence characteristics, time of convergence, error correction capability, and stability to the trained patterns, and it is observed that the results are encouraging to pursue further research in this direction.

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In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/7af9-r597

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