The Experts below are selected from a list of 18852 Experts worldwide ranked by ideXlab platform
Huaguang Zhang - One of the best experts on this subject based on the ideXlab platform.
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exponential stability and stabilization of delayed memristive neural networks based on quadratic Convex Combination method
IEEE Transactions on Neural Networks, 2016Co-Authors: Zhanshan Wang, Sanbo Ding, Zhanjun Huang, Huaguang ZhangAbstract:This paper is concerned with the exponential stability and stabilization of memristive neural networks (MNNs) with delays. First, we present some generalized double-integral inequalities, which include some existing inequalities as their special cases. Second, combining with quadratic Convex Combination method, these double-integral inequalities are employed to formulate a delay-dependent stability condition for MNNs with delays. Third, a state-dependent switching control law is obtained for MNNs with delays based on the proposed stability conditions. The desired feedback gain matrices are accomplished by solving a set of linear matrix inequalities. Finally, the feasibility and effectiveness of the proposed results are tested by two numerical examples.
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an enhanced input delay approach to sampled data stabilization of t s fuzzy systems via mixed Convex Combination
Nonlinear Dynamics, 2014Co-Authors: Feisheng Yang, Huaguang Zhang, Yingchun WangAbstract:The problem of sampled-data control is investigated for Takagi–Sugeno (T–S) fuzzy systems with aperiodic sampling intervals based on an enhanced input-delay approach. Delay-dependent stability and stabilizability conditions for the closed-loop continuous nonuniformly sampled-data fuzzy systems are derived by constructing a novel discontinuous Lyapunov–Krasovskii (L–K) functional, which makes good use of not only the upper bound on the variable sampling interval, but also its sawtooth structure information about varying input delay often ignored in previous results. A bounding technique combined by reciprocally Convex technics and linear Convex Combination is presented for acquiring the time derivative of the functional, wherein Jensen’s inequality and Wirtinger’s inequality are integratively employed. And a feasible solution of the obtained criterion formulated as parameterized linear matrix inequalities is ultimately conceived. A numerical example is given to show the effectiveness of the proposed method.
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stability analysis for neural networks with time varying delay based on quadratic Convex Combination
IEEE Transactions on Neural Networks, 2013Co-Authors: Huaguang Zhang, Feisheng Yang, Xiaodong Liu, Qingling ZhangAbstract:In this paper, a novel method is developed for the stability problem of a class of neural networks with time-varying delay. New delay-dependent stability criteria in terms of linear matrix inequalities for recurrent neural networks with time-varying delay are derived by the newly proposed augmented simple Lyapunov-Krasovski functional. Different from previous results by using the first-order Convex Combination property, our derivation applies the idea of second-order Convex Combination and the property of quadratic Convex function which is given in the form of a lemma without resorting to Jensen's inequality. A numerical example is provided to verify the effectiveness and superiority of the presented results.
Lianglin Xiong - One of the best experts on this subject based on the ideXlab platform.
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quadratic stabilization of switched uncertain linear systems a Convex Combination approach
IEEE CAA Journal of Automatica Sinica, 2019Co-Authors: Yufang Chang, Guisheng Zhai, Lianglin XiongAbstract:We consider quadratic stabilization for a class of switched systems which are composed of a finite set of continuous-time linear subsystems with norm bounded uncertainties. Under the assumption that there is no single quadratically stable subsystem, if a Convex Combination of subsystems is quadratically stable, then we propose a state-dependent switching law, based on the Convex Combination of subsystems, such that the entire switched linear system is quadratically stable. When the state information is not available, we extend the discussion to designing an output-dependent switching law by constructing a robust Luenberger observer for each subsystem.
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stochastic stability analysis for neutral type markov jump neural networks with additive time varying delays via a new reciprocally Convex Combination inequality
International Journal of Systems Science, 2019Co-Authors: Lianglin Xiong, Haiyang Zhang, Zhipeng Qiu, Guanghao JiangAbstract:ABSTRACTThis paper investigates the stochastic stability problem for a class of neutral-type Markov jump neural networks with additive time-varying delays. Firstly, to derive a tighter lower bound of the reciprocally Convex quadratic terms, a new reciprocally Convex Combination inequality is established by using parameters transformation approach. Secondly, by fully considering the peculiarity of various time-varying delays and Markov jumping parameters, an eligible stochastic Lyapunov–Krasovskii functional is constructed. Then, by employing the new reciprocally Convex Combination inequality and other analytical techniques, some novel stability criteria are provided in the forms of linear matrix inequalities. Finally, four illustrated examples are given to verify the effectiveness and feasibility of the proposed methods.
Feisheng Yang - One of the best experts on this subject based on the ideXlab platform.
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matrix quadratic Convex Combination for stability of linear systems with time varying delay via new augmented lyapunov functional
World Congress on Intelligent Control and Automation, 2016Co-Authors: Feisheng YangAbstract:The paper addresses the delay-dependent stability problem for linear systems with time-varying delay. Novel delay-dependent stability criteria in terms of linear matrix inequalities for systems with state time-varying delay are derived by the newly proposed augmented Lyapunov-Krasovski (L-K) functional. A matrix-type quadratic Convex Combination approach is introduced to prove the negative definiteness of the derivative of the L-K functional along with the trajectory of the delayed system. Different from previous results by using the first order Convex Combination, our derivation applies the idea of second order Convex Combination, and the property of quadratic Convex function without resorting to the Jensen's inequality.
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an enhanced input delay approach to sampled data stabilization of t s fuzzy systems via mixed Convex Combination
Nonlinear Dynamics, 2014Co-Authors: Feisheng Yang, Huaguang Zhang, Yingchun WangAbstract:The problem of sampled-data control is investigated for Takagi–Sugeno (T–S) fuzzy systems with aperiodic sampling intervals based on an enhanced input-delay approach. Delay-dependent stability and stabilizability conditions for the closed-loop continuous nonuniformly sampled-data fuzzy systems are derived by constructing a novel discontinuous Lyapunov–Krasovskii (L–K) functional, which makes good use of not only the upper bound on the variable sampling interval, but also its sawtooth structure information about varying input delay often ignored in previous results. A bounding technique combined by reciprocally Convex technics and linear Convex Combination is presented for acquiring the time derivative of the functional, wherein Jensen’s inequality and Wirtinger’s inequality are integratively employed. And a feasible solution of the obtained criterion formulated as parameterized linear matrix inequalities is ultimately conceived. A numerical example is given to show the effectiveness of the proposed method.
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stability analysis for neural networks with time varying delay based on quadratic Convex Combination
IEEE Transactions on Neural Networks, 2013Co-Authors: Huaguang Zhang, Feisheng Yang, Xiaodong Liu, Qingling ZhangAbstract:In this paper, a novel method is developed for the stability problem of a class of neural networks with time-varying delay. New delay-dependent stability criteria in terms of linear matrix inequalities for recurrent neural networks with time-varying delay are derived by the newly proposed augmented simple Lyapunov-Krasovski functional. Different from previous results by using the first-order Convex Combination property, our derivation applies the idea of second-order Convex Combination and the property of quadratic Convex function which is given in the form of a lemma without resorting to Jensen's inequality. A numerical example is provided to verify the effectiveness and superiority of the presented results.
Zhiguang Feng - One of the best experts on this subject based on the ideXlab platform.
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improved stability condition for takagi sugeno fuzzy systems with time varying delay
IEEE Transactions on Systems Man and Cybernetics, 2017Co-Authors: Zhiguang Feng, Wei Xing ZhengAbstract:In this paper, the stability analysis problem of Takagi–Sugeno fuzzy systems with time-varying delay is investigated. By utilizing the Wirtinger-based integral inequality and the improved reciprocally Convex Combination technique, an improved stability condition is derived in terms of linear matrix inequalities. A numerical example is given to demonstrate the efficiency of the obtained result.
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on reachable set estimation of delay markovian jump systems with partially known transition probabilities
Journal of The Franklin Institute-engineering and Applied Mathematics, 2016Co-Authors: Zhiguang Feng, Wei Xing ZhengAbstract:Abstract In this paper, the problem of reachable set estimation of discrete-time Markovian jump systems with time-varying delay is addressed. By applying the improved reciprocally Convex Combination approach to bound the forward difference of double summation and the reciprocally Convex Combination approach to bound the forward difference of triple summation, a sufficient condition on reachable set estimation is first derived for delay Markovian jump systems with completely known transition probabilities. Then the result is extended to delay Markovian jump systems with partially known transition probabilities. Based on the criterion, a less conservative stability criterion for delay Markovian jump systems is also obtained as a by-product. In order to illustrate the effectiveness and the reduced conservatism of the proposed results, three numerical examples are presented.
Haiyang Zhang - One of the best experts on this subject based on the ideXlab platform.
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stochastic stability analysis for neutral type markov jump neural networks with additive time varying delays via a new reciprocally Convex Combination inequality
International Journal of Systems Science, 2019Co-Authors: Lianglin Xiong, Haiyang Zhang, Zhipeng Qiu, Guanghao JiangAbstract:ABSTRACTThis paper investigates the stochastic stability problem for a class of neutral-type Markov jump neural networks with additive time-varying delays. Firstly, to derive a tighter lower bound of the reciprocally Convex quadratic terms, a new reciprocally Convex Combination inequality is established by using parameters transformation approach. Secondly, by fully considering the peculiarity of various time-varying delays and Markov jumping parameters, an eligible stochastic Lyapunov–Krasovskii functional is constructed. Then, by employing the new reciprocally Convex Combination inequality and other analytical techniques, some novel stability criteria are provided in the forms of linear matrix inequalities. Finally, four illustrated examples are given to verify the effectiveness and feasibility of the proposed methods.
