한빛사 논문
영남대학교
Tae H. Leea, Ju H. Parka,*, O.M. Kwonb, S.M. Leec
a Nonlinear Dynamics Group, Department of Electrical Engineering, Yeungnam University, 214-1 Dae-Dong, Kyongsan 712-749, Republic of Korea
b School of Electrical Engineering, Chungbuk National University, 52 Naesudong-ro, Cheongju 361-763, Republic of Korea
c Department of Electronic Engineering, Daegu University, Gyungsan 712-714, Republic of Korea
*Corresponding author
Abstract
This study examines the state estimation problem for neural networks with a time-varying delay. Unlike other studies, the sampled-data with stochastic sampling is used to design the state estimator using a novel approach that divides the bounding of the activation function into two subintervals. To fully use the sawtooth structure characteristics of the sampling input delay, a discontinuous Lyapunov functional is proposed based on the extended Wirtinger inequality. The desired estimator gain can be characterized in terms of the solution to linear matrix inequalities (LMIs). Finally, the proposed method is applied to two numerical examples to show the effectiveness of our result.
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Keywords : State estimator; Neural networks; Time-varying delay; Sampled-data; Stochastic sampling
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