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Neural Computing and Applications

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01.02.2011 | Original Article

The effect of weight fault on associative networks

verfasst von: Andrew Chi-Sing Leung, Pui Fai Sum, Kevin Ho

Erschienen in: Neural Computing and Applications | Ausgabe 1/2011

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Abstract

In the past three decades, the properties of associative networks has been extensively investigated. However, most existing results focus on the fault-free networks only. In implementation, network faults can be exhibited in different forms, such as open weight fault and multiplicative weight noise. This paper studies the effect of weight fault on the performance of the bidirectional associative memory (BAM) model when multiplicative weight noise and open weight fault present. Assuming that connection weights are corrupted by these two common fault models, we study how many number of pattern pairs can be stored in a faulty BAM. Since one of important feature of associative network is error correction, we also study the number of pattern pairs can be stored in a faulty BAM when there are some errors in the initial stimulus pattern.

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Titel: The effect of weight fault on associative networks
verfasst von: Andrew Chi-Sing Leung
Pui Fai Sum
Kevin Ho
Publikationsdatum: 01.02.2011
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 1/2011
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0351-2

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