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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7-8/2014

01.12.2014 | Original Article

Mean square input-to-state stability of a general class of stochastic recurrent neural networks with Markovian switching

verfasst von: Yong Xu, Weiwei Luo, Kai Zhong, Song Zhu

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2014

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Abstract

The paper presents M-matrix algebraic criteria for the input-to-state stability of a class of stochastic recurrent neural networks with Markovian switching. First, the criterion without the time delays is derived. Then, the result is extended to time-varying condition. The criterion also ensures globally exponential stability if there is no input term. These conditions are the improvement and extension of the existing results in the literature. A numerical example is given to demonstrate the effectiveness of the proposed algebraic criteria.
Vorheriger Artikel Short-term prediction of traffic flow using a binary neural network
Nächster Artikel Backstepping control for output-constrained nonlinear systems based on nonlinear mapping

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Metadaten
Titel
Mean square input-to-state stability of a general class of stochastic recurrent neural networks with Markovian switching
verfasst von
Yong Xu
Weiwei Luo
Kai Zhong
Song Zhu
Publikationsdatum
01.12.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7-8/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1649-2

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