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Neural Computing and Applications

Erschienen in:

01.01.2014 | ICONIP 2012

Expanded HP memristor model and simulation in STDP learning

verfasst von: Yu Dai, Chuandong Li, Hui Wang

Erschienen in: Neural Computing and Applications | Ausgabe 1/2014

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Abstract

Based on the classical HP memristor found by HP Lab, this paper presents an expanded model that making fully consideration of the influence of R _on, that is, R _on is the similar order of magnitude of R _off. Simulations proved that in some particular conditions, the hysteresis effect of the expanded model is the same as HP memristor. A comparison was made between these two models under some given conditions. Then, we built several simulations to test the classical characteristics of the expanded HP memristor. Simulation results demonstrate that the expanded model is superior to the original in some aspects like easy switching and power saving. At last, we applied the expanded HP memristor in STDP learning simulation, which shows it is a good candidate for neural network when a threshold voltage function is proposed.

Vorheriger Artikel Analyses for L p [a, b]-norm approximation capability of flexible approximate identity neural networks

Nächster Artikel Impulsive control for synchronizing delayed discrete complex networks with switching topology

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Titel: Expanded HP memristor model and simulation in STDP learning
verfasst von: Yu Dai
Chuandong Li
Hui Wang
Publikationsdatum: 01.01.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1467-y

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