Work

Non-random weight dilution in high performance associative memories

Year: 2002

Type: article

Abstract: The consequences of two techniques for symmetrically diluting the weights of the standard Hopfield architecture associative memory model, trained using a non-Hebbian learning rule, are examined. This paper reports experimental investigations into the effect of dilution on factors such as: pattern stability and attractor performance. It is concluded that these networks maintain a reasonable level of performance at fairly high dilution rates.

Source: University of Hertfordshire Research Archive (University of Hertfordshire)

Authors: S. P. Turvey, Stephen P. Hunt, N. Davey, R. Frank

Institution: University of Hertfordshire

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W259320724

FWCI:

Cites: 14

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Related to: 10