NDLI: An efficient learning algorithm for associative memories

Content Provider	IEEE Xplore Digital Library
Author	Yingquan Wu Batalama, S.N.
Copyright Year	1990
Abstract	Associative memories (AMs) can be implemented using networks with or without feedback. We utilize a two-layer feedforward neural network and propose a learning algorithm that efficiently implements the association rule of a bipolar AM. The hidden layer of the network employs p neurons where p is the number of prototype patterns. In the first layer, the input pattern activates at most one hidden layer neuron or "winner". In the second layer, the "winner" associates the input pattern to the corresponding prototype pattern. The underlying association principle is minimum Hamming distance and the proposed scheme can be viewed also as an approximately minimum Hamming distance decoder. Theoretical analysis supported by simulations indicates that, in comparison with other suboptimum minimum Hamming distance association schemes, the proposed structure exhibits the following favorable characteristics: 1) it operates in one-shot which implies no convergence-time requirements; 2) it does not require any feedback; and 3) our case studies show that it exhibits superior performance to the popular linear system in a saturated mode. The network also exhibits 4) exponential capacity and 5) easy performance assessment (no asymptotic analysis is necessary). Finally, since it does not require any hidden layer interconnections or tree-search operations, it exhibits low structural as well as operational complexity.
Sponsorship	IEEE Computational Intelligence Society
Starting Page	1058
Ending Page	1066
Page Count	9
File Size	239462
File Format	PDF
ISSN	10459227
Volume Number	11
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-09-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Associative memory Hamming distance Neurons Prototypes Performance analysis Neurofeedback Neural networks Feedforward neural networks Association rules Decoding
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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