The Experts below are selected from a list of 18639 Experts worldwide ranked by ideXlab platform

Weihua Sheng - One of the best experts on this subject based on the ideXlab platform.

  • exponential h synchronization and state Estimation for chaotic systems via a unified model
    IEEE Transactions on Neural Networks, 2013
    Co-Authors: Senlin Zhang, Shiyou Zheng, Weihua Sheng
    Abstract:

    In this paper, H∞ synchronization and state Estimation problems are considered for different types of chaotic systems. A unified model consisting of a linear Dynamic system and a bounded static nonlinear operator is employed to describe these chaotic systems, such as Hopfield neural networks, cellular neural networks, Chua's circuits, unified chaotic systems, Qi systems, chaotic recurrent multilayer perceptrons, etc. Based on the H∞ performance analysis of this unified model using the linear matrix inequality approach, novel state feedback controllers are established not only to guarantee exponentially stable synchronization between two unified models with different initial conditions but also to reduce the effect of external disturbance on the synchronization Error to a minimal H∞ norm constraint. The state Estimation problem is then studied for the same unified model, where the purpose is to design a state estimator to estimate its states through available output measurements so that the exponential stability of the Estimation Error Dynamic systems is guaranteed and the influence of noise on the Estimation Error is limited to the lowest level. The parameters of these controllers and filters are obtained by solving the eigenvalue problem. Most chaotic systems can be transformed into this unified model, and H∞ synchronization controllers and state estimators for these systems are designed in a unified way. Three numerical examples are provided to show the usefulness of the proposed H∞ synchronization and state Estimation conditions.

Senlin Zhang - One of the best experts on this subject based on the ideXlab platform.

  • exponential h synchronization and state Estimation for chaotic systems via a unified model
    IEEE Transactions on Neural Networks, 2013
    Co-Authors: Senlin Zhang, Shiyou Zheng, Weihua Sheng
    Abstract:

    In this paper, H∞ synchronization and state Estimation problems are considered for different types of chaotic systems. A unified model consisting of a linear Dynamic system and a bounded static nonlinear operator is employed to describe these chaotic systems, such as Hopfield neural networks, cellular neural networks, Chua's circuits, unified chaotic systems, Qi systems, chaotic recurrent multilayer perceptrons, etc. Based on the H∞ performance analysis of this unified model using the linear matrix inequality approach, novel state feedback controllers are established not only to guarantee exponentially stable synchronization between two unified models with different initial conditions but also to reduce the effect of external disturbance on the synchronization Error to a minimal H∞ norm constraint. The state Estimation problem is then studied for the same unified model, where the purpose is to design a state estimator to estimate its states through available output measurements so that the exponential stability of the Estimation Error Dynamic systems is guaranteed and the influence of noise on the Estimation Error is limited to the lowest level. The parameters of these controllers and filters are obtained by solving the eigenvalue problem. Most chaotic systems can be transformed into this unified model, and H∞ synchronization controllers and state estimators for these systems are designed in a unified way. Three numerical examples are provided to show the usefulness of the proposed H∞ synchronization and state Estimation conditions.

Shiyou Zheng - One of the best experts on this subject based on the ideXlab platform.

  • exponential h synchronization and state Estimation for chaotic systems via a unified model
    IEEE Transactions on Neural Networks, 2013
    Co-Authors: Senlin Zhang, Shiyou Zheng, Weihua Sheng
    Abstract:

    In this paper, H∞ synchronization and state Estimation problems are considered for different types of chaotic systems. A unified model consisting of a linear Dynamic system and a bounded static nonlinear operator is employed to describe these chaotic systems, such as Hopfield neural networks, cellular neural networks, Chua's circuits, unified chaotic systems, Qi systems, chaotic recurrent multilayer perceptrons, etc. Based on the H∞ performance analysis of this unified model using the linear matrix inequality approach, novel state feedback controllers are established not only to guarantee exponentially stable synchronization between two unified models with different initial conditions but also to reduce the effect of external disturbance on the synchronization Error to a minimal H∞ norm constraint. The state Estimation problem is then studied for the same unified model, where the purpose is to design a state estimator to estimate its states through available output measurements so that the exponential stability of the Estimation Error Dynamic systems is guaranteed and the influence of noise on the Estimation Error is limited to the lowest level. The parameters of these controllers and filters are obtained by solving the eigenvalue problem. Most chaotic systems can be transformed into this unified model, and H∞ synchronization controllers and state estimators for these systems are designed in a unified way. Three numerical examples are provided to show the usefulness of the proposed H∞ synchronization and state Estimation conditions.

Chengchew Lim - One of the best experts on this subject based on the ideXlab platform.

  • fault detection filtering for nonhomogeneous markovian jump systems via a fuzzy approach
    IEEE Transactions on Fuzzy Systems, 2018
    Co-Authors: Peng Shi, Chengchew Lim
    Abstract:

    This paper investigates the problem of the fault detection filter design for nonhomogeneous Markovian jump systems by a Takagi–Sugeno fuzzy approach. Attention is focused on the construction of a fault detection filter to ensure the Estimation Error Dynamic stochastically stable, and the prescribed performance requirement can be satisfied. The designed fuzzy model-based fault detection filter can guarantee the sensitivity of the residual signal to faults and the robustness of the external disturbances. By using the cone complementarity linearization algorithm, the existence conditions for the design of fault detection filters are provided. Meanwhile, the Error between the residual signal and the fault signal is made as small as possible. Finally, a practical application is given to illustrate the effectiveness of the proposed technique.

Peng Shi - One of the best experts on this subject based on the ideXlab platform.

  • fault detection filtering for nonhomogeneous markovian jump systems via a fuzzy approach
    IEEE Transactions on Fuzzy Systems, 2018
    Co-Authors: Peng Shi, Chengchew Lim
    Abstract:

    This paper investigates the problem of the fault detection filter design for nonhomogeneous Markovian jump systems by a Takagi–Sugeno fuzzy approach. Attention is focused on the construction of a fault detection filter to ensure the Estimation Error Dynamic stochastically stable, and the prescribed performance requirement can be satisfied. The designed fuzzy model-based fault detection filter can guarantee the sensitivity of the residual signal to faults and the robustness of the external disturbances. By using the cone complementarity linearization algorithm, the existence conditions for the design of fault detection filters are provided. Meanwhile, the Error between the residual signal and the fault signal is made as small as possible. Finally, a practical application is given to illustrate the effectiveness of the proposed technique.