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Enhancing the Delta Training Rule for a Single Layer Feedforward Heteroassociative Memory Neural Network

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 1
Year of Publication: 2014
Authors:
Omar Waleed Abdulwahhab
10.5120/16759-6314

Omar Waleed Abdulwahhab. Article: Enhancing the Delta Training Rule for a Single Layer Feedforward Heteroassociative Memory Neural Network. International Journal of Computer Applications 96(1):23-27, June 2014. Full text available. BibTeX

@article{key:article,
	author = {Omar Waleed Abdulwahhab},
	title = {Article: Enhancing the Delta Training Rule for a Single Layer Feedforward Heteroassociative Memory Neural Network},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {1},
	pages = {23-27},
	month = {June},
	note = {Full text available}
}

Abstract

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1. 6% and 3. 2% special partially described noisy inputs patterns are presented.

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