Extended dissipative estimator design for uncertain switched delayed neural networks via a novel triple integral inequality

Wenqian Xie, Hong Zhu, Shouming Zhong, Dian Zhang, Kaibo Shi and Jun Cheng

Applied Mathematics and Computation, 2018, vol. 335, issue C, 82-102

Abstract: This paper addresses the problem of extended dissipative estimator design for uncertain switched neural networks (SNNs) with mixed time-varying delays and general activation functions. Firstly, for dealing with triple integral term, a new integral inequality is derived. Secondly, based on the theory of convex combination, we propose a novel flexible delay division method and corresponding modified Lyapunov–Krasovskii functional (LKF) is established. Thirdly, a switching estimator design approach is contributed, which ensures that the resulting augmented system is extended dissipative. Combining the extended reciprocally convex technique with Wirtinger-based integral inequality, improved delay-dependent exponential stability criterion is obtained. Finally, a example with two cases is provided to illustrate the feasibility and effectiveness of the developed theoretical results.

Keywords: Extended dissipativity; State estimation; Switched neural networks; Delay division method; Exponential stability; Triple integral inequality (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:335:y:2018:i:c:p:82-102

DOI: 10.1016/j.amc.2018.04.037

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