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Neural Netw. 2018 Feb;98:192-202. doi: 10.1016/j.neunet.2017.11.007. Epub 2017 Nov 24.

pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays.

Author information

1
School of Financial Mathematics & Statistics, Guangdong University of Finance, Guangzhou 510521, China. Electronic address: 27-010@gduf.edu.cn.
2
School of Financial Mathematics & Statistics, Guangdong University of Finance, Guangzhou 510521, China.

Abstract

Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Itô's differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and Cauchy-Schwarz inequality, we derive some sufficient conditions for the mean square exponential stability of the above systems. The obtained results improve and extend previous works on memristor-based or usual neural networks dynamical systems. Four numerical examples are provided to illustrate the effectiveness of the proposed results.

KEYWORDS:

BAM neural networks; Memristor; Stochastic; th moment exponential stability

PMID:
29268196
DOI:
10.1016/j.neunet.2017.11.007
[Indexed for MEDLINE]

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