DocumentCode :
1160104
Title :
Associative recall using a contraction operator
Author :
Stubberud, Allen R. ; Thomas, Robert J.
Author_Institution :
Dept. of Electr. Eng., California Univ., Irvine, CA, USA
Volume :
36
Issue :
5
fYear :
1989
fDate :
5/1/1989 12:00:00 AM
Firstpage :
682
Lastpage :
686
Abstract :
An associative memory can be defined as a transformation between two sets. Under mild conditions, the associative recall problem can be formulated as that of solving an equation of the form y=f (x), where y is known and the corresponding value x is not. Here, the associative recall problem is formulated in this way, and conditions on f are developed such that a contraction operator can be developed which solves the given equation. A specific piecewise-linear function is then chosen, and its associative recall properties are discussed. This associative memory is shown to converge rapidly and to have noise rejection properties and some learning capability
Keywords :
content-addressable storage; neural nets; associative memory; associative recall problem; contraction operator; converge rapidly; learning capability; noise rejection properties; piecewise-linear function; transformation between two sets; Associative memory; Convergence; Discrete transforms; Equations; Mathematics; Neural networks; Piecewise linear techniques; Problem-solving;
fLanguage :
English
Journal_Title :
Circuits and Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0098-4094
Type :
jour
DOI :
10.1109/31.31316
Filename :
31316
Link To Document :
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