NDLI: High performance associative memory with distance based training algorithm for character recognition

Content Provider	IEEE Xplore Digital Library
Author	Ming-Jung Seow Asari, V.K.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. & Comput. Univ., Old Dominion Univ., Norfolk, VA, USA (Ming-Jung Seow; Asari, V.K.)
Abstract	The consequence of reducing the impact of the synaptic weights from neurons farther away from the neuron under consideration on a modular two-dimensional Hopfield network using Hebbian learning rule is examined for image processing applications. A generalized modular architecture is developed by defining each module with a group of neighboring neurons and all modules communicating with each other. A spatially decaying distance factor is introduced into the Hebbian rule to reduce the effect of neurons from farther modules. A biologically inspired visual perception concept has been adopted for defining the variation of the distance factor. The performance of the new technique is evaluated by conducting several experiments on character images and it is observed that the proposed method increases the learning ability and convergence rate of the network. The nature of the distance factor helps the removal of several synaptic weights farther away from a particular neuron and this leads to the reduction of complexity of the network in terms of both software and hardware implementation.
Sponsorship	Int. Neural Network Soc. IEEE Neural Networks Soc
Starting Page	1185
Ending Page	1188
File Size	312895
Page Count	4
File Format	PDF
ISBN	0780378989
ISSN	10987576
DOI	10.1109/IJCNN.2003.1223860
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Associative memory Character recognition Neurons Biological neural networks Image processing Application software Convergence Hopfield neural networks Computer networks Electronic mail
Content Type	Text
Resource Type	Article

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