NDLI: Characterization of a higher-order associative memory that uses product units

Content Provider	IEEE Xplore Digital Library
Author	Jung-Hua Wang
Copyright Year	1993
Description	Author affiliation: Dept. of Electr. Eng., Nat. Taiwan Ocean Univ., Keelung, Taiwan (Jung-Hua Wang)
Abstract	The characteristics of a novel 3-layer feedforward neural network that can be used as a higher-order associative memory is studied. The network structure consists of a hidden layer that contains product units for which each input is raised to a power determined by a trainable weight. The operation of the network consists of three steps: 1) preprocess the prescribed associative vectors and determine the principal connection weights (i.e., the first phase learning); 2) estimate the required number of product units and connections based on the results from (1); and 3) train the network using the backpropagation algorithm until satisfactory recall accuracy is achieved. The use of this two-phase learning is shown to enable us to achieve: 1) learning without requiring long training time; and 2) major reduction in the number of connection weights. Various interesting characteristics of the network, including input noise tolerance and fault tolerance, can be seen in this network.
Starting Page	636
Ending Page	639
File Size	277615
Page Count	4
File Format	PDF
ISBN	0780309111
DOI	10.1109/ICSMC.1993.384946
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-10-17
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Associative memory Neural networks Feedforward neural networks Polynomials Oceans Phase estimation Noise reduction Phase noise Fault tolerance Backpropagation
Content Type	Text
Resource Type	Article

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