Huasheng Tan

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This paper is concerned with the problem of robust stability and H1 filter design for neutral stochastic neural networks with parameter uncertainties and timevarying delay. The parameter uncertainties are assumed to be norm-bounded. With the Lyapunov-krasovskii theory, several delay-dependent stability conditions are obtained in terms of liner matrix(More)
This paper presents the delay-dependent $$H_\infty$$ H ∞ and generalized H 2 filters design for stochastic neural networks with time-varying delay and noise disturbance. The stochastic neural networks under consideration are subject to time-varying delay in both the state and measurement equations. The aim is to design a stable full-order linear filter(More)
This paper concerns the design of the non-fragile H<sub>&#x221E;</sub> filter for the fuzzy system with time-varying delays. Attention is focused on the design of the filter which is subject to gain variations, such that the filtering system is robustly stable with a prescribed H<sub>&#x221E;</sub> performance level for all admissible uncertainties. A(More)
The problem of robust L<sub>2</sub>-L<sub>&#x221E;</sub> filter design of uncertain neutral stochastic systems with Markovian jumping parameters and time delay is discussed in this paper. The parameter uncertainties are assumed to be norm-bounded. Based on the Lyapunov-krasovskii theory and generalized Finsler lemma, a delay-dependent stability condition is(More)
This paper is concerned with the asymptotical stability analysis for stochastic static neural networks with time-varying delay. Here, the time derivative of the time-varying delay is no longer required to be smaller than one. With the use of convex polyhedron method, by constructing appropriate Lyapunov-Krasovskii functional, several delay-dependent(More)
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