首页|Reliable exponential H-infinity filtering for a class of switched reaction-diffusion neural networks
Reliable exponential H-infinity filtering for a class of switched reaction-diffusion neural networks
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NSTL
Elsevier
In this paper, the reliable exponential H-infinity filtering issue is studied for switched reaction-diffusion neural networks subject to exterior interference. The purpose is to design a Luenberger observer to make sure that the filtering error system possesses a pre-defined exponential H-infinity interference-rejection level against possible sensor failures. An analysis result on the exponential H-infinity performance is presented by the use of a Lyapunov functional together with a few inequalities. On its basis, a linear matrix inequalities-based design scheme for the Luenberger observer is proposed by getting rid of the nonlinear terms composed of the Lyapunov matrix, the gain matrix, and an uncertainty matrix caused by the sensor failures. In the case when the factors of sensor failures and reaction-diffusion are not concerned, the design scheme is shown to be an improvement over an existing design scheme. Finally, two examples are given to demonstrate the applicability and reduced conservatism of the obtained results, respectively. (c) 2021 Elsevier Inc. All rights reserved.
NSTL
Elsevier
