H∞ performance state estimation of delayed static neural networks based on an improved proportional-integral estimator

Guoqiang Tan, Zhanshan Wang and Cong Li

Applied Mathematics and Computation, 2020, vol. 370, issue C

Abstract: In this paper, an improved proportional-integral (PI) estimator is presented to analyze the problem of H∞ performance state estimation of static neural networks with disturbance. An exponential gain term is added to the PI estimator, which leads to the convenience of analysis and design. In order to guarantee the H∞ performance state estimation, a less conservative delay-dependent criterion is derived by using an improved reciprocally convex inequality. Finally, simulation results are given to verify the advantage of the presented approach.

Keywords: H∞ performance; State estimation; Time-varying delay; Static neural networks; Proportional-integral estimator with exponential gain term (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:370:y:2020:i:c:s0096300319309002

DOI: 10.1016/j.amc.2019.124908

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