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Neural Computing and Applications

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01.12.2013 | Original Article

Exponential convergence for HRNNs with continuously distributed delays in the leakage terms

verfasst von: Zhibin Chen, Mingquan Yang

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2013

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Abstract

This paper considers exponential convergence for a class of high-order recurrent neural networks (HRNNs) with continuously distributed delays in the leakage terms (i.e., “leakage delays”). Without assuming the boundedness on the activation functions, some sufficient conditions are derived to ensure that all solutions of this system converge exponentially to zero point by using Lyapunov functional method and differential inequality techniques, which are new and complement previously known results. In particular, we propose a new approach to prove the exponential convergence of HRNNs with continuously distributed delays in the leakage terms. Moreover, an example is given to show the effectiveness of the proposed method and results.

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Titel: Exponential convergence for HRNNs with continuously distributed delays in the leakage terms
verfasst von: Zhibin Chen
Mingquan Yang
Publikationsdatum: 01.12.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1172-2

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