H
Huisheng Shu
Researcher at Donghua University
Publications - 76
Citations - 3344
Huisheng Shu is an academic researcher from Donghua University. The author has contributed to research in topics: Nonlinear system & Filtering problem. The author has an hindex of 30, co-authored 67 publications receiving 3079 citations. Previous affiliations of Huisheng Shu include Chinese Ministry of Education & Penn State College of Information Sciences and Technology.
Papers
More filters
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Brief paper: Robust filtering with stochastic nonlinearities and multiple missing measurements
TL;DR: The purpose of the addressed filtering problem is to design a filter such that, for the admissible random measurement missing, stochastic disturbances, norm-bounded uncertainties as well as Stochastic nonlinearities, the error dynamics of the filtering process is exponentially mean-square stable.
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Quantized $H_{\infty }$ Control for Nonlinear Stochastic Time-Delay Systems With Missing Measurements
TL;DR: In this paper, the quantized H∞ control problem for a class of nonlinear stochastic time-delay network-based systems with probabilistic data missing is investigated, where the measured output and the input signals are quantized by two logarithmic quantizers, respectively.
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$H_{\infty}$ State Estimation for Complex Networks With Uncertain Inner Coupling and Incomplete Measurements
TL;DR: The first attempt to characterize the uncertainties entering into the inner coupling matrix is made with the aid of the interval matrix approach, and a novel measurement model is proposed to account for these phenomena occurring with individual probability.
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$H_{\infty}$ State Estimation for Discrete-Time Complex Networks With Randomly Occurring Sensor Saturations and Randomly Varying Sensor Delays
TL;DR: In this paper, sufficient conditions are established under which the addressed state estimation problem is recast as solving a convex optimization problem via the semidefinite programming method.
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Robust stability analysis of generalized neural networks with discrete and distributed time delays
TL;DR: In this paper, robust global stability analysis for generalized neural networks (GNNs) with both discrete and distributed delays is addressed. But the authors assume that the parameter uncertainties are time invariant and bounded, and belong to given compact sets.