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Yurong Liu
Researcher at Yangzhou University
Publications - 139
Citations - 10394
Yurong Liu is an academic researcher from Yangzhou University. The author has contributed to research in topics: Linear matrix inequality & Exponential stability. The author has an hindex of 46, co-authored 116 publications receiving 8756 citations. Previous affiliations of Yurong Liu include Anhui Polytechnic University.
Papers
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Global exponential stability of generalized recurrent neural networks with discrete and distributed delays
TL;DR: A linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally exponentially stable, and the existence and uniqueness of the equilibrium point under mild conditions is proved.
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Global Synchronization for Discrete-Time Stochastic Complex Networks With Randomly Occurred Nonlinearities and Mixed Time Delays
Zidong Wang,Yao Wang,Yurong Liu +2 more
TL;DR: By constructing a novel Lyapunov-like matrix functional, the idea of delay fractioning is applied to deal with the addressed synchronization analysis problem and several delay-dependent sufficient conditions are obtained which ensure the asymptotic synchronization in the mean square sense for the discrete-time stochastic complex networks with time delays.
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Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays
TL;DR: A synchronization problem is investigated for an array of coupled complex discrete-time networks with the simultaneous presence of both the discrete and distributed time delays and an LMI approach is developed for the state estimation problem.
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Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
TL;DR: It is shown that the addressed stochastic Cohen-Grossberg neural networks with mixed delays are globally asymptotically stable in the mean square if two LMIs are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox.
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On global asymptotic stability of neural networks with discrete and distributed delays
TL;DR: In this article, the authors investigated the global asymptotic stability analysis problem for a class of neural networks with discrete and distributed time-delays and derived sufficient conditions for the neural networks to be globally stable in terms of a linear matrix inequality.