The Experts below are selected from a list of 65631 Experts worldwide ranked by ideXlab platform

Lin Xiao - One of the best experts on this subject based on the ideXlab platform.

  • computing time varying quadratic optimization with finite time convergence and noise tolerance a unified framework for zeroing neural network
    IEEE Transactions on Neural Networks, 2019
    Co-Authors: Lin Xiao, Mingxing Duan
    Abstract:

    Zeroing neural network (ZNN), as a powerful calculating tool, is extensively applied in various computation and optimization fields. Convergence and noise-tolerance performance are always pursued and investigated in the ZNN field. Up to now, there are no unified ZNN models that simultaneously achieve the finite-time convergence and inherent noise tolerance for computing time-varying quadratic optimization problems, although this superior property is highly demanded in practical applications. In this paper, for computing time-varying quadratic optimization within finite-time convergence in the presence of various additive noises, a new framework for ZNN is Designed to fill this gap in a unified manner. Specifically, different from the previous Design Formulas either possessing finite-time convergence or possessing noise-tolerance performance, a new Design Formula with finite-time convergence and noise tolerance is proposed in a unified framework (and thus called unified Design Formula). Then, on the basis of the unified Design Formula, a unified ZNN (UZNN) is, thus, proposed and investigated in the unified framework of ZNN for computing time-varying quadratic optimization problems in the presence of various additive noises. In addition, theoretical analyses of the unified Design Formula and the UZNN model are given to guarantee the finite-time convergence and inherent noise tolerance. Computer simulation results verify the superior property of the UZNN model for computing time-varying quadratic optimization problems, as compared with the previously proposed ZNN models.

  • a new recurrent neural network with noise tolerance and finite time convergence for dynamic quadratic minimization
    Neurocomputing, 2018
    Co-Authors: Lin Xiao, Jian Yang, Zhijun Zhang
    Abstract:

    Abstract To solve dynamic quadratic minimization, a nonlinearly activated integration Design Formula is first proposed in this paper with additive noises considered. Then, on the basis of such a Design Formula, a new recurrent neural network (RNN) is established to solve the dynamic quadratic minimization. Compared with the conventional Zhang neural network (ZNN) for this problem, the proposed RNN model possesses the outstanding finite-time convergence and the inherently noise-tolerant performance, and is thus called the versatile RNN (VRNN) model. In addition, the global stability, the finite-time convergence and the denoising ability of the VRNN model are proved by rigorous mathematical results in theory. The upper bound of the finite convergence time for the VRNN model is also analytically derived. Numerical simulative results are presented to validate the efficacy of the VRNN model, as well as its superior performance to the conventional ZNN model for dynamic quadratic minimization in the presence of various additive noises.

  • accelerating a recurrent neural network to finite time convergence using a new Design Formula and its application to time varying matrix square root
    Journal of The Franklin Institute-engineering and Applied Mathematics, 2017
    Co-Authors: Lin Xiao
    Abstract:

    Abstract In this paper, a new Design Formula is presented to accelerate the convergence speed of a recurrent neural network, and applied to time-varying matrix square root finding in real time. Then, according to such a new Design Formula, a finite-time Zhang neural network (FTZNN) is proposed and investigated for finding time-varying matrix square root. In comparison with the original Zhang neural network (ZNN) model, the FTZNN model makes a breakthrough in the convergence performance (i.e., from infinite time to finite time). In addition, theoretical analyses of the Design Formula and the FTZNN model are provided in details. Comparative results further verify the superiority of the proposed FTZNN model to the original ZNN model for finding time-varying matrix square root.

  • a new Design Formula exploited for accelerating zhang neural network and its application to time varying matrix inversion
    Theoretical Computer Science, 2016
    Co-Authors: Lin Xiao
    Abstract:

    Online solution to time-varying matrix inverse is further investigated by proposing a new Design Formula, which can accelerate Zhang neural network (ZNN) to finite-time convergence. Compared with the existing recurrent neural networks e.g., the gradient neural networks (GNN), and the original Zhang neural network, the proposed neural network (termed finite-time ZNN, FTZNN) makes a breakthrough in the convergence performance (i.e., from infinite time to finite time). In addition, different from the previous processing method (i.e., choosing a better nonlinear activation to accelerate convergence speed), this paper subtly proposes a new Design Formula to accelerate the original ZNN model and Design the new FTZNN model. Besides, theoretical analyses of the Design Formula and the FTZNN model are given in detail. Simulative results substantiate the effectiveness and superiorness of the proposed FTZNN model for online time-varying matrix inversion, as compared with the GNN model and the original ZNN model.

Long Jin - One of the best experts on this subject based on the ideXlab platform.

  • proposing developing and verification of a novel discrete time zeroing neural network for solving future augmented sylvester matrix equation
    Journal of The Franklin Institute-engineering and Applied Mathematics, 2020
    Co-Authors: Yang Shi, Long Jin, Jipeng Qiang
    Abstract:

    Abstract In this paper, a novel discrete-time advance zeroing neural network (DT-AZNN) model is proposed, developed and investigated for solving future augmented Sylvester matrix equation (F-ASME). First of all, based on the advance zeroing neural network (AZNN) Design Formula, a novel continuous-time advance zeroing neural network (CT-AZNN) model is shown for solving continuous-time augmented Sylvester matrix equation (CT-ASME). Secondly, a recently published discretization Formula is further investigated with the optimal sampling gap of the discretization Formula proposed. Then, for solving F-ASME, a novel DT-AZNN model is proposed based on the discretization Formula. Theoretical analyses on the convergence property and the perturbation suppression performance of the DT-AZNN model are provided. Moreover, comparative numerical experimental results are conducted to prove the effectiveness and robustness of the proposed DT-AZNN model for solving F-ASME.

  • noise tolerant znn models for solving time varying zero finding problems a control theoretic approach
    IEEE Transactions on Automatic Control, 2017
    Co-Authors: Long Jin, Yunong Zhang, Yinyan Zhang
    Abstract:

    This technical note proposes a noise-tolerant zeroing neural network (NTZNN) Design Formula, and shows how recurrent (and recursive) methods for solving time-varying problems can be Designed from the viewpoint of control. The NTZNN Design Formula provides a control-theoretic framework to deal with the convergence, stability and robustness issues of continuous-time (and discrete-time) models. NTZNN models derived from the proposed Design Formula demonstrate their advantages when applied to solving time-varying zero-finding problems in the presence of noises.

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

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

  • a new recurrent neural network with noise tolerance and finite time convergence for dynamic quadratic minimization
    Neurocomputing, 2018
    Co-Authors: Lin Xiao, Jian Yang, Zhijun Zhang
    Abstract:

    Abstract To solve dynamic quadratic minimization, a nonlinearly activated integration Design Formula is first proposed in this paper with additive noises considered. Then, on the basis of such a Design Formula, a new recurrent neural network (RNN) is established to solve the dynamic quadratic minimization. Compared with the conventional Zhang neural network (ZNN) for this problem, the proposed RNN model possesses the outstanding finite-time convergence and the inherently noise-tolerant performance, and is thus called the versatile RNN (VRNN) model. In addition, the global stability, the finite-time convergence and the denoising ability of the VRNN model are proved by rigorous mathematical results in theory. The upper bound of the finite convergence time for the VRNN model is also analytically derived. Numerical simulative results are presented to validate the efficacy of the VRNN model, as well as its superior performance to the conventional ZNN model for dynamic quadratic minimization in the presence of various additive noises.

Mingxing Duan - One of the best experts on this subject based on the ideXlab platform.

  • computing time varying quadratic optimization with finite time convergence and noise tolerance a unified framework for zeroing neural network
    IEEE Transactions on Neural Networks, 2019
    Co-Authors: Lin Xiao, Mingxing Duan
    Abstract:

    Zeroing neural network (ZNN), as a powerful calculating tool, is extensively applied in various computation and optimization fields. Convergence and noise-tolerance performance are always pursued and investigated in the ZNN field. Up to now, there are no unified ZNN models that simultaneously achieve the finite-time convergence and inherent noise tolerance for computing time-varying quadratic optimization problems, although this superior property is highly demanded in practical applications. In this paper, for computing time-varying quadratic optimization within finite-time convergence in the presence of various additive noises, a new framework for ZNN is Designed to fill this gap in a unified manner. Specifically, different from the previous Design Formulas either possessing finite-time convergence or possessing noise-tolerance performance, a new Design Formula with finite-time convergence and noise tolerance is proposed in a unified framework (and thus called unified Design Formula). Then, on the basis of the unified Design Formula, a unified ZNN (UZNN) is, thus, proposed and investigated in the unified framework of ZNN for computing time-varying quadratic optimization problems in the presence of various additive noises. In addition, theoretical analyses of the unified Design Formula and the UZNN model are given to guarantee the finite-time convergence and inherent noise tolerance. Computer simulation results verify the superior property of the UZNN model for computing time-varying quadratic optimization problems, as compared with the previously proposed ZNN models.