The Experts below are selected from a list of 1960689 Experts worldwide ranked by ideXlab platform
Xiaohui Liu - One of the best experts on this subject based on the ideXlab platform.
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asymptotic stability for neural networks with mixed time delays the discrete time case
Neural Networks, 2009Co-Authors: Yurong Liu, Zidong Wang, Xiaohui LiuAbstract:This paper is concerned with the stability Analysis Problem for a new class of discrete-time recurrent neural networks with mixed time-delays. The mixed time-delays that consist of both the discrete and distributed time-delays are addressed, for the first time, when analyzing the asymptotic stability for discrete-time neural networks. The activation functions are not required to be differentiable or strictly monotonic. The existence of the equilibrium point is first proved under mild conditions. By constructing a new Lyapnuov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the discrete-time neural networks to be globally asymptotically stable. As an extension, we further consider the stability Analysis Problem for the same class of neural networks but with state-dependent stochastic disturbances. All the conditions obtained are expressed in terms of LMIs whose feasibility can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition.
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exponential stability of uncertain stochastic neural networks with mixed time delays
Chaos Solitons & Fractals, 2007Co-Authors: Zidong Wang, Jianan Fang, Stanislao Lauria, Xiaohui LiuAbstract:This paper is concerned with the global exponential stability Analysis Problem for a class of stochastic neural networks with mixed time-delays and parameter uncertainties. The mixed delays comprise discrete and distributed time-delays, the parameter uncertainties are norm-bounded, and the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion. The purpose of the stability Analysis Problem is to derive easy-to-test criteria under which the delayed stochastic neural network is globally, robustly, exponentially stable in the mean square for all admissible parameter uncertainties. By resorting to the Lyapunov–Krasovskii stability theory and the stochastic Analysis tools, sufficient stability conditions are established by using an efficient linear matrix inequality (LMI) approach. The proposed criteria can be checked readily by using recently developed numerical packages, where no tuning of parameters is required. An example is provided to demonstrate the usefulness of the proposed criteria.
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on global exponential stability of generalized stochastic neural networks with mixed time delays
Neurocomputing, 2006Co-Authors: Yurong Liu, Zidong Wang, Xiaohui LiuAbstract:Abstract This paper is concerned with the global exponential stability Analysis Problem for a general class of stochastic neural networks with mixed time-delays. The mixed time-delays under consideration comprise both the discrete time-varying delays and the distributed time-delays. The main purpose of this paper is to establish easily verifiable conditions under which the delayed stochastic neural network is exponentially stable in the mean square in the presence of both the discrete and distributed delays. By employing a new Lyapunov–Krasovskii functional and conducting stochastic Analysis, a linear matrix inequality (LMI) approach is developed to derive the criteria of the exponential stability. Furthermore, the main results are specialized to deal with the Analysis Problem for the global asymptotic stability within the same LMI framework. The proposed criteria can be readily checked by using some standard numerical packages such as the Matlab LMI toolbox. A simple example is provided to demonstrate the effectiveness and applicability of the proposed testing criteria.
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on global asymptotic stability of neural networks with discrete and distributed delays
Physics Letters A, 2005Co-Authors: Zidong Wang, Yurong Liu, Xiaohui LiuAbstract:Abstract In this Letter, the global asymptotic stability Analysis Problem is investigated for a class of neural networks with discrete and distributed time-delays. The purpose of the Problem is to determine the asymptotic stability by employing some easy-to-test conditions. It is shown, via the Lyapunov–Krasovskii stability theory, that the class of neural networks under consideration is globally asymptotically stable if a quadratic matrix inequality involving several parameters is feasible. Furthermore, a linear matrix inequality (LMI) approach is exploited to transform the addressed stability Analysis Problem into a convex optimization Problem, and sufficient conditions for the neural networks to be globally asymptotically stable are then derived in terms of a linear matrix inequality, which can be readily solved by using the Matlab LMI toolbox. Two numerical examples are provided to show the usefulness of the proposed global stability condition.
Yang Tang - One of the best experts on this subject based on the ideXlab platform.
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robust stability for genetic regulatory networks with linear fractional uncertainties
Communications in Nonlinear Science and Numerical Simulation, 2012Co-Authors: Wenbing Zhang, Jianan Fang, Yang TangAbstract:Abstract In this paper, the asymptotic stability Analysis Problem for a class of delayed genetic regulatory networks (GRNs) with linear fractional uncertainties and stochastic perturbations is studied. By employing a more effective Lyapunov functional and using a lemma to estimate the derivative of the Lyapunov functional, some new sufficient conditions for the stability Problem of GRNs are derived in terms of linear matrix inequality (LMI). Finally, two numerical examples are used to demonstrate the usefulness of the main results and less conservatism of the derived conditions.
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stochastic stability of markovian jumping genetic regulatory networks with mixed time delays
Applied Mathematics and Computation, 2011Co-Authors: Wenbing Zhang, Jianan Fang, Yang TangAbstract:Abstract In this paper, the stability Analysis Problem is investigated for a class of Markovian jumping genetic regulatory networks (GRNs) with mixed time delays (discrete time delays and distributed time delays) and stochastic perturbations. The main purpose of the addressed stability Analysis Problem is to establish some easy-to-verify conditions under which the dynamics of the true concentrations of the messenger ribonucleic acid and protein is asymptotically stable. By utilizing a more general Lyapunov–Krasovskii functional based on the idea of “delay decomposing” and the LMI (linear matrix inequality) technique, we derive sufficient delay-dependent conditions ensuring the asymptotically stability of the GRNs with mixed time delays and noise perturbations in terms of LMI. Finally, simulation examples are exploited to illustrate the effectiveness of the developed theoretical results.
Zidong Wang - One of the best experts on this subject based on the ideXlab platform.
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on robust stability of stochastic genetic regulatory networks with time delays a delay fractioning approach
Systems Man and Cybernetics, 2010Co-Authors: Yao Wang, Zidong Wang, Jinling LiangAbstract:Robust stability serves as an important regulation mechanism in system biology and synthetic biology. In this paper, the robust stability Analysis Problem is investigated for a class of nonlinear delayed genetic regulatory networks with parameter uncertainties and stochastic perturbations. The nonlinear function describing the feedback regulation satisfies the sector condition, the time delays exist in both translation and feedback regulation processes, and the state-dependent Brownian motions are introduced to reflect the inherent intrinsic and extrinsic noise perturbations. The purpose of the addressed stability Analysis Problem is to establish some easy-to-verify conditions under which the dynamics of the true concentrations of the messenger ribonucleic acid (mRNA) and protein is asymptotically stable irrespective of the norm-bounded modeling errors. By utilizing a new Lyapunov functional based on the idea of ?delay fractioning?, we employ the linear matrix inequality (LMI) technique to derive delay-dependent sufficient conditions ensuring the robust stability of the gene regulatory networks. Note that the obtained results are formulated in terms of LMIs that can easily be solved using standard software packages. Simulation examples are exploited to illustrate the effectiveness of the proposed design procedures.
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asymptotic stability for neural networks with mixed time delays the discrete time case
Neural Networks, 2009Co-Authors: Yurong Liu, Zidong Wang, Xiaohui LiuAbstract:This paper is concerned with the stability Analysis Problem for a new class of discrete-time recurrent neural networks with mixed time-delays. The mixed time-delays that consist of both the discrete and distributed time-delays are addressed, for the first time, when analyzing the asymptotic stability for discrete-time neural networks. The activation functions are not required to be differentiable or strictly monotonic. The existence of the equilibrium point is first proved under mild conditions. By constructing a new Lyapnuov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the discrete-time neural networks to be globally asymptotically stable. As an extension, we further consider the stability Analysis Problem for the same class of neural networks but with state-dependent stochastic disturbances. All the conditions obtained are expressed in terms of LMIs whose feasibility can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition.
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exponential stability of uncertain stochastic neural networks with mixed time delays
Chaos Solitons & Fractals, 2007Co-Authors: Zidong Wang, Jianan Fang, Stanislao Lauria, Xiaohui LiuAbstract:This paper is concerned with the global exponential stability Analysis Problem for a class of stochastic neural networks with mixed time-delays and parameter uncertainties. The mixed delays comprise discrete and distributed time-delays, the parameter uncertainties are norm-bounded, and the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion. The purpose of the stability Analysis Problem is to derive easy-to-test criteria under which the delayed stochastic neural network is globally, robustly, exponentially stable in the mean square for all admissible parameter uncertainties. By resorting to the Lyapunov–Krasovskii stability theory and the stochastic Analysis tools, sufficient stability conditions are established by using an efficient linear matrix inequality (LMI) approach. The proposed criteria can be checked readily by using recently developed numerical packages, where no tuning of parameters is required. An example is provided to demonstrate the usefulness of the proposed criteria.
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on global exponential stability of generalized stochastic neural networks with mixed time delays
Neurocomputing, 2006Co-Authors: Yurong Liu, Zidong Wang, Xiaohui LiuAbstract:Abstract This paper is concerned with the global exponential stability Analysis Problem for a general class of stochastic neural networks with mixed time-delays. The mixed time-delays under consideration comprise both the discrete time-varying delays and the distributed time-delays. The main purpose of this paper is to establish easily verifiable conditions under which the delayed stochastic neural network is exponentially stable in the mean square in the presence of both the discrete and distributed delays. By employing a new Lyapunov–Krasovskii functional and conducting stochastic Analysis, a linear matrix inequality (LMI) approach is developed to derive the criteria of the exponential stability. Furthermore, the main results are specialized to deal with the Analysis Problem for the global asymptotic stability within the same LMI framework. The proposed criteria can be readily checked by using some standard numerical packages such as the Matlab LMI toolbox. A simple example is provided to demonstrate the effectiveness and applicability of the proposed testing criteria.
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on global asymptotic stability of neural networks with discrete and distributed delays
Physics Letters A, 2005Co-Authors: Zidong Wang, Yurong Liu, Xiaohui LiuAbstract:Abstract In this Letter, the global asymptotic stability Analysis Problem is investigated for a class of neural networks with discrete and distributed time-delays. The purpose of the Problem is to determine the asymptotic stability by employing some easy-to-test conditions. It is shown, via the Lyapunov–Krasovskii stability theory, that the class of neural networks under consideration is globally asymptotically stable if a quadratic matrix inequality involving several parameters is feasible. Furthermore, a linear matrix inequality (LMI) approach is exploited to transform the addressed stability Analysis Problem into a convex optimization Problem, and sufficient conditions for the neural networks to be globally asymptotically stable are then derived in terms of a linear matrix inequality, which can be readily solved by using the Matlab LMI toolbox. Two numerical examples are provided to show the usefulness of the proposed global stability condition.
Roma Tauler - One of the best experts on this subject based on the ideXlab platform.
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multivariate curve resolution 50 years addressing the mixture Analysis Problem a review
Analytica Chimica Acta, 2021Co-Authors: A. De Juan, Roma TaulerAbstract:Multivariate Curve Resolution (MCR) covers a wide span of algorithms designed to tackle the mixture Analysis Problem by expressing the original data through a bilinear model of pure component meaningful contributions. Since the seminal work by Lawton and Sylvestre in 1971, MCR methods are dynamically evolving to adapt to a wealth of diverse and demanding scientific scenarios. To do so, essential concepts, such as basic constraints, have been revisited and new modeling tasks, mathematical properties and domain-specific information have been incorporated; the initial underlying bilinear model has evolved into a flexible framework where hybrid bilinear/multilinear models can coexist, the regular data structures have undergone a turn of the screw and incomplete multisets and matrix and tensor combinations can be now analyzed. Back to the fundamentals, the theoretical core of the MCR methodology is deeply understood due to the thorough studies about the ambiguity phenomenon. The adaptation of the method to new analytical measurements and scientific domains is continuous. At this point of the story, MCR can be considered a mature yet lively methodology, where many steps forward can still be taken.
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multivariate curve resolution mcr solving the mixture Analysis Problem
Analytical Methods, 2014Co-Authors: A. De Juan, Joaquim Jaumot, Roma TaulerAbstract:This article is a tutorial that focuses on the main aspects to be considered when applying Multivariate Curve Resolution to analyze multicomponent systems, particularly when the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) algorithm is used. These aspects include general MCR comments on the potential fields of application and construction of data structures and details linked to each of the steps in the application workflow of the MCR-ALS algorithm (e.g., selection of initial estimates, choice and application of constraints, quality parameters of models and assessment of ambiguity,…). Two examples with downloadable data sets are shown for orientation on the practical use of this methodology.
Yurong Liu - One of the best experts on this subject based on the ideXlab platform.
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asymptotic stability for neural networks with mixed time delays the discrete time case
Neural Networks, 2009Co-Authors: Yurong Liu, Zidong Wang, Xiaohui LiuAbstract:This paper is concerned with the stability Analysis Problem for a new class of discrete-time recurrent neural networks with mixed time-delays. The mixed time-delays that consist of both the discrete and distributed time-delays are addressed, for the first time, when analyzing the asymptotic stability for discrete-time neural networks. The activation functions are not required to be differentiable or strictly monotonic. The existence of the equilibrium point is first proved under mild conditions. By constructing a new Lyapnuov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the discrete-time neural networks to be globally asymptotically stable. As an extension, we further consider the stability Analysis Problem for the same class of neural networks but with state-dependent stochastic disturbances. All the conditions obtained are expressed in terms of LMIs whose feasibility can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition.
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on global exponential stability of generalized stochastic neural networks with mixed time delays
Neurocomputing, 2006Co-Authors: Yurong Liu, Zidong Wang, Xiaohui LiuAbstract:Abstract This paper is concerned with the global exponential stability Analysis Problem for a general class of stochastic neural networks with mixed time-delays. The mixed time-delays under consideration comprise both the discrete time-varying delays and the distributed time-delays. The main purpose of this paper is to establish easily verifiable conditions under which the delayed stochastic neural network is exponentially stable in the mean square in the presence of both the discrete and distributed delays. By employing a new Lyapunov–Krasovskii functional and conducting stochastic Analysis, a linear matrix inequality (LMI) approach is developed to derive the criteria of the exponential stability. Furthermore, the main results are specialized to deal with the Analysis Problem for the global asymptotic stability within the same LMI framework. The proposed criteria can be readily checked by using some standard numerical packages such as the Matlab LMI toolbox. A simple example is provided to demonstrate the effectiveness and applicability of the proposed testing criteria.
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on global asymptotic stability of neural networks with discrete and distributed delays
Physics Letters A, 2005Co-Authors: Zidong Wang, Yurong Liu, Xiaohui LiuAbstract:Abstract In this Letter, the global asymptotic stability Analysis Problem is investigated for a class of neural networks with discrete and distributed time-delays. The purpose of the Problem is to determine the asymptotic stability by employing some easy-to-test conditions. It is shown, via the Lyapunov–Krasovskii stability theory, that the class of neural networks under consideration is globally asymptotically stable if a quadratic matrix inequality involving several parameters is feasible. Furthermore, a linear matrix inequality (LMI) approach is exploited to transform the addressed stability Analysis Problem into a convex optimization Problem, and sufficient conditions for the neural networks to be globally asymptotically stable are then derived in terms of a linear matrix inequality, which can be readily solved by using the Matlab LMI toolbox. Two numerical examples are provided to show the usefulness of the proposed global stability condition.
