The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jinde Cao - One of the best experts on this subject based on the ideXlab platform.
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finite time stability analysis for markovian jump memristive neural networks with partly unknown transition probabilities
IEEE Transactions on Neural Networks, 2017Co-Authors: Jinde CaoAbstract:This paper is concerned with the finite-time Stochastically stability (FTSS) analysis of Markovian jump memristive neural networks with partly unknown transition probabilities. In the neural networks, there exist a group of modes determined by Markov chain, and thus, the Markovian jump was taken into consideration and the concept of FTSS is first introduced for the memristive model. By introducing a Markov switching Lyapunov functional and Stochastic analysis theory, an FTSS test procedure is proposed, from which we can conclude that the settling time function is a Stochastic Variable and its expectation is finite. The system under consideration is quite general since it contains completely known and completely unknown transition probabilities as two special cases. More importantly, a nonlinear measure method was introduced to verify the uniqueness of the equilibrium point; compared with the fixed point Theorem that has been widely used in the existing results, this method is more easy to implement. Besides, the delay interval was divided into four subintervals, which make full use of the information of the subsystems upper bounds of the time-varying delays. Finally, the effectiveness and superiority of the proposed method is demonstrated by two simulation examples.
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exponential h filtering analysis for discrete time switched neural networks with random delays using sojourn probabilities
Science China-technological Sciences, 2016Co-Authors: Jinde Cao, R Rakkiyappan, K Maheswari, A ChandrasekarAbstract:This paper is concerned with the exponential $H_{\infty}$ filtering problem for a class of discrete-time switched neural networks with random time-varying delays based on the sojourn-probability-dependent method. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with random time-varying delays which are characterized by introducing a Bernoulli Stochastic Variable. Based on the derived $H_\infty$ performance analysis results, the $H_\infty$ filter design is formulated in terms of Linear Matrix Inequalities (LMIs). Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design procedure.
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exponential synchronization of coupled Stochastic memristor based neural networks with time varying probabilistic delay coupling and impulsive delay
IEEE Transactions on Neural Networks, 2016Co-Authors: Haibo Bao, Ju H Park, Jinde CaoAbstract:This paper deals with the exponential synchronization of coupled Stochastic memristor-based neural networks with probabilistic time-varying delay coupling and time-varying impulsive delay. There is one probabilistic transmittal delay in the delayed coupling that is translated by a Bernoulli Stochastic Variable satisfying a conditional probability distribution. The disturbance is described by a Wiener process. Based on Lyapunov functions, Halanay inequality, and linear matrix inequalities, sufficient conditions that depend on the probability distribution of the delay coupling and the impulsive delay were obtained. Numerical simulations are used to show the effectiveness of the theoretical results.
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neural networks letter delay distribution dependent state estimation for discrete time Stochastic neural networks with random delay
Neural Networks, 2011Co-Authors: Haibo Bao, Jinde CaoAbstract:This paper is concerned with the state estimation problem for a class of discrete-time Stochastic neural networks (DSNNs) with random delays. The effect of both variation range and distribution probability of the time delay are taken into account in the proposed approach. The Stochastic disturbances are described in terms of a Brownian motion and the time-varying delay is characterized by introducing a Bernoulli Stochastic Variable. By employing a Lyapunov-Krasovskii functional, sufficient delay-distribution-dependent conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimator which can be checked readily by the Matlab toolbox. The main feature of the results obtained in this paper is that they are dependent on not only the bound but also the distribution probability of the time delay, and we obtain a larger allowance variation range of the delay, hence our results are less conservative than the traditional delay-independent ones. One example is given to illustrate the effectiveness of the proposed result.
R Rakkiyappan - One of the best experts on this subject based on the ideXlab platform.
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exponential h filtering analysis for discrete time switched neural networks with random delays using sojourn probabilities
Science China-technological Sciences, 2016Co-Authors: Jinde Cao, R Rakkiyappan, K Maheswari, A ChandrasekarAbstract:This paper is concerned with the exponential $H_{\infty}$ filtering problem for a class of discrete-time switched neural networks with random time-varying delays based on the sojourn-probability-dependent method. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with random time-varying delays which are characterized by introducing a Bernoulli Stochastic Variable. Based on the derived $H_\infty$ performance analysis results, the $H_\infty$ filter design is formulated in terms of Linear Matrix Inequalities (LMIs). Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design procedure.
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pinning sampled data control for synchronization of complex networks with probabilistic time varying delays using quadratic convex approach
Neurocomputing, 2015Co-Authors: R Rakkiyappan, N SakthivelAbstract:This paper addresses pinning sampled-data synchronization problem for complex dynamical networks with probabilistic time-varying coupling delays and control packet loss. The sampling period considered here is assumed to be less than a given bound. By introducing a Bernoulli distributed Stochastic Variable, the information of probabilistic time-varying delay is transformed into the deterministic time-varying delay with Stochastic parameters. A new Lyapunov-Krasovskii functional (LKF) is constructed and by using quadratic convex approach, reciprocal convex technique and Jensen?s inequality, sufficient conditions for the synchronization of complex networks are derived. Based on the average dwell-time method and delay-probability-distribution-dependent condition, the synchronization criterion is derived in terms of linear matrix inequalities (LMIs). Finally, numerical examples are provided to illustrate the effectiveness of the proposed techniques.
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exponential stability of markovian jumping Stochastic cohen grossberg neural networks with mode dependent probabilistic time varying delays and impulses
Neurocomputing, 2014Co-Authors: R Rakkiyappan, A Chandrasekar, S Lakshmanan, Ju H ParkAbstract:This paper deals with robust exponential stability of Markovian jumping Stochastic Cohen-Grossberg neural networks (MJSCGNNs) with mode-dependent probabilistic time-varying delays, continuously distributed delays and impulsive perturbations. By construction of novel Lyapunov-Krasovskii functional having the triple integral terms, the double integral terms having the positive definite matrices dependent on the system mode and MJSCGNNs system transformation Variables, new delay-dependent exponential stability conditions are derived in terms of linear matrix inequalities (LMIs). By establishing a Stochastic Variable with Bernoulli distribution, the information of probabilistic time-varying delay is considered and transformed into one with deterministic time-varying delay and Stochastic parameters. Furthermore, a mode-dependent mean square robust exponential stability criterion is derived by constriction of new Lyapunov-Krasovskii functional having modes in the integral terms, linear matrix inequalities and some Stochastic analysis techniques. Finally, two numerical examples are provided to show the effectiveness of the proposed methods.
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delay dependent robust exponential state estimation of markovian jumping fuzzy hopfield neural networks with mixed random time varying delays
Communications in Nonlinear Science and Numerical Simulation, 2011Co-Authors: P Balasubramaniam, V Vembarasan, R RakkiyappanAbstract:This paper investigates delay-dependent robust exponential state estimation of Markovian jumping fuzzy neural networks with mixed random time-varying delay. In this paper, the Takagi–Sugeno (T–S) fuzzy model representation is extended to the robust exponential state estimation of Markovian jumping Hopfield neural networks with mixed random time-varying delays. Moreover probabilistic delay satisfies a certain probability-distribution. By introducing a Stochastic Variable with a Bernoulli distribution, the neural networks with random time delays is transformed into one with deterministic delays and Stochastic parameters. The main purpose is to estimate the neuron states, through available output measurements such that for all admissible time delays, the dynamics of the estimation error is globally exponentially stable in the mean square. Based on the Lyapunov–Krasovskii functional and Stochastic analysis approach, several delay-dependent robust state estimators for such T–S fuzzy Markovian jumping Hopfield neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. The unknown gain matrix is determined by solving a delay-dependent LMI. Finally some numerical examples are provided to demonstrate the effectiveness of the proposed method.
Haibo Bao - One of the best experts on this subject based on the ideXlab platform.
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exponential synchronization of coupled Stochastic memristor based neural networks with time varying probabilistic delay coupling and impulsive delay
IEEE Transactions on Neural Networks, 2016Co-Authors: Haibo Bao, Ju H Park, Jinde CaoAbstract:This paper deals with the exponential synchronization of coupled Stochastic memristor-based neural networks with probabilistic time-varying delay coupling and time-varying impulsive delay. There is one probabilistic transmittal delay in the delayed coupling that is translated by a Bernoulli Stochastic Variable satisfying a conditional probability distribution. The disturbance is described by a Wiener process. Based on Lyapunov functions, Halanay inequality, and linear matrix inequalities, sufficient conditions that depend on the probability distribution of the delay coupling and the impulsive delay were obtained. Numerical simulations are used to show the effectiveness of the theoretical results.
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neural networks letter delay distribution dependent state estimation for discrete time Stochastic neural networks with random delay
Neural Networks, 2011Co-Authors: Haibo Bao, Jinde CaoAbstract:This paper is concerned with the state estimation problem for a class of discrete-time Stochastic neural networks (DSNNs) with random delays. The effect of both variation range and distribution probability of the time delay are taken into account in the proposed approach. The Stochastic disturbances are described in terms of a Brownian motion and the time-varying delay is characterized by introducing a Bernoulli Stochastic Variable. By employing a Lyapunov-Krasovskii functional, sufficient delay-distribution-dependent conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimator which can be checked readily by the Matlab toolbox. The main feature of the results obtained in this paper is that they are dependent on not only the bound but also the distribution probability of the time delay, and we obtain a larger allowance variation range of the delay, hence our results are less conservative than the traditional delay-independent ones. One example is given to illustrate the effectiveness of the proposed result.
Raul Cardenas - One of the best experts on this subject based on the ideXlab platform.
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generation capacity expansion planning under hydro uncertainty using Stochastic mixed integer programming and scenario reduction
Power and Energy Society General Meeting, 2015Co-Authors: Ignacio Aravena, Raul CardenasAbstract:Generation Capacity Expansion Planning (GCEP) is the process of deciding on a set of optimal new investments in generation capacity to adequately supply future loads, while satisfying technical and reliability constraints. This paper shows the application of Stochastic Mixed-Integer Programming (SMIP) to account for hydrological uncertainty in GCEP for the Chilean Central Interconnected System, using a two-stage SMIP multi-period model with investments and optimal power flow (OPF). The substantial computational challenges posed by GCEP imply compromising between the detail of the Stochastic hydrological Variables and the detail of the OPF. We selected a subset of hydrological scenarios to represent the historical hydro variability using moment-based scenario reduction techniques. The tradeoff between modeling accuracy and computational complexity was explored both regarding the simplification of the MIP problem and the differences in the Variables of interest. Using a simplified OPF model we found the difference of using a subset of hydro scenarios to be small when compared with using a full representation of the Stochastic Variable. Overall, SMIP with scenario reduction provided optimal capacity expansion plans whose investment plus expected operational costs were between 1.3% and 1.9% cheaper than using a deterministic approach and proved to be more robust to hydro variability.
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generation capacity expansion planning under hydro uncertainty using Stochastic mixed integer programming and scenario reduction
IEEE Transactions on Power Systems, 2015Co-Authors: Esteban Gil, Ignacio Aravena, Raul CardenasAbstract:Generation capacity expansion planning (GCEP) is the process of deciding on a set of optimal new investments in generation capacity to adequately supply future loads, while satisfying technical and reliability constraints. This paper shows the application of Stochastic mixed-integer programming (SMIP) to account for hydrological uncertainty in GCEP for the Chilean Central Interconnected System, using a two-stage SMIP multi-period model with investments and optimal power flow (OPF). The substantial computational challenges posed by GCEP imply compromising between the detail of the Stochastic hydrological Variables and the detail of the OPF. We selected a subset of hydrological scenarios to represent the historical hydro variability using moment-based scenario reduction techniques. The tradeoff between modeling accuracy and computational complexity was explored both regarding the simplification of the MIP problem and the differences in the Variables of interest. Using a simplified OPF model, we found the difference of using a subset of hydro scenarios to be small when compared with using a full representation of the Stochastic Variable. Overall, SMIP with scenario reduction provided optimal capacity expansion plans whose investment plus expected operational costs were between 1.3% and 1.9% cheaper than using a deterministic approach and proved to be more robust to hydro variability.
Ju H Park - One of the best experts on this subject based on the ideXlab platform.
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exponential synchronization of coupled Stochastic memristor based neural networks with time varying probabilistic delay coupling and impulsive delay
IEEE Transactions on Neural Networks, 2016Co-Authors: Haibo Bao, Ju H Park, Jinde CaoAbstract:This paper deals with the exponential synchronization of coupled Stochastic memristor-based neural networks with probabilistic time-varying delay coupling and time-varying impulsive delay. There is one probabilistic transmittal delay in the delayed coupling that is translated by a Bernoulli Stochastic Variable satisfying a conditional probability distribution. The disturbance is described by a Wiener process. Based on Lyapunov functions, Halanay inequality, and linear matrix inequalities, sufficient conditions that depend on the probability distribution of the delay coupling and the impulsive delay were obtained. Numerical simulations are used to show the effectiveness of the theoretical results.
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finite time energy to peak filtering for markov jump repeated scalar non linear systems with packet dropouts
Iet Control Theory and Applications, 2014Co-Authors: Hao Shen, Zhengguang Wu, Ju H ParkAbstract:This study is concerned with the finite-time energy-to-peak filtering problem for Markov jump repeated scalar non-linear systems with packet dropouts. The packet dropout phenomenon occurs in a random way and is modelled by a Stochastic Variable satisfying the Bernoulli distribution. The purpose of the study is to design a filter such that the resulting filtering error system is Stochastically finite-time bounded, and a prescribed energy-to-peak performance level is achieved over a finite time interval. By using the mode-dependent diagonally dominant Lyapunov function approach, some strict linear matrix inequality-based conditions are established for the existence of an admissible filter, and a corresponding explicit parameterisation of such a filter is obtained. Finally, a numerical example with simulation is presented to demonstrate the effectiveness of our proposed approach.
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exponential stability of markovian jumping Stochastic cohen grossberg neural networks with mode dependent probabilistic time varying delays and impulses
Neurocomputing, 2014Co-Authors: R Rakkiyappan, A Chandrasekar, S Lakshmanan, Ju H ParkAbstract:This paper deals with robust exponential stability of Markovian jumping Stochastic Cohen-Grossberg neural networks (MJSCGNNs) with mode-dependent probabilistic time-varying delays, continuously distributed delays and impulsive perturbations. By construction of novel Lyapunov-Krasovskii functional having the triple integral terms, the double integral terms having the positive definite matrices dependent on the system mode and MJSCGNNs system transformation Variables, new delay-dependent exponential stability conditions are derived in terms of linear matrix inequalities (LMIs). By establishing a Stochastic Variable with Bernoulli distribution, the information of probabilistic time-varying delay is considered and transformed into one with deterministic time-varying delay and Stochastic parameters. Furthermore, a mode-dependent mean square robust exponential stability criterion is derived by constriction of new Lyapunov-Krasovskii functional having modes in the integral terms, linear matrix inequalities and some Stochastic analysis techniques. Finally, two numerical examples are provided to show the effectiveness of the proposed methods.
