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Neural Computing and Applications

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01.05.2013 | Original Article

Associate learning and correcting in a memristive neural network

verfasst von: Ling Chen, Chuandong Li, Xin Wang, Shukai Duan

Erschienen in: Neural Computing and Applications | Ausgabe 6/2013

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Abstract

This paper further studies the ability of the associate learning and self-correcting in a memristive artificial neural network (ANN). Different from the existing models, the present ANN contains the multiply-threshold neurons, the discrete charge-controlled memristors, and a new learning law named the max-input-feedback (MIF). We shall demonstrate the processes of the associative learning and associative correcting via a modified Pavlov experiment where more conditioning factors are considered. We also make some comparisons of MIF with spike-timing-dependent plasticity and back-propagation and show that MIF learning law is suitable to fast learning.

Vorheriger Artikel Feed-back neural networks with discrete weights

Nächster Artikel A rough margin-based one class support vector machine

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Titel: Associate learning and correcting in a memristive neural network
verfasst von: Ling Chen
Chuandong Li
Xin Wang
Shukai Duan
Publikationsdatum: 01.05.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 6/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0868-7

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