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

  • new dtznn model for future minimization with cube steady state error pattern using taylor finite difference formula
    International Conference on Intelligent Control and Information Processing, 2015
    Co-Authors: Yunong Zhang, Ying Fang, Bolin Liao, Tianjian Qiao
    Abstract:

    In this paper, a discrete-time Zhang neural network (DTZNN) model, discretized from continuous-time Zhang neural network, is proposed and investigated for performing the online future minimization (OFM). In order to approximate more accurately the 1st-order derivative in computation and discretize more effectively the continuous-time Zhang neural network, a new Taylor-type numerical differentiation formula, together with the optimal sampling-gap rule, is presented and utilized to obtain the Taylor-type DTZNN model. For comparison, Euler-type DTZNN model and Newton Iteration, with an interesting link being found, are also presented. Moreover, theoretical results of stability and convergence are presented, which show that the steady-state residual errors of the presented Taylor-type DTZNN model, Euler-type DTZNN model and Newton Iteration have a pattern of 0(t3), 0(t2) and 0(t), respectively, with t denoting the sampling gap. Numerical experimental results further substantiate the effectiveness and advantages of the Taylor-type DTZNN model for solving the OFM problem.

  • zhang neural network and its application to Newton Iteration for matrix square root estimation
    Neural Computing and Applications, 2012
    Co-Authors: Yunong Zhang, Binghuang Cai, Yiwen Yang, Dongsheng Guo
    Abstract:

    A special class of recurrent neural networks (RNN) has recently been proposed by Zhang et al. for solving online time-varying matrix problems. Being different from conventional gradient-based neural networks (GNN), such RNN (termed specifically as Zhang neural networks, ZNN) are designed based on matrix-valued error functions, instead of scalar-valued norm-based energy functions. In this paper, we generalize and further investigate the ZNN model for time-varying matrix square root finding. For the purpose of possible hardware (e.g., digital circuit) realization, a discrete-time ZNN model is constructed and developed, which incorporates Newton Iteration as a special case. Besides, to obtain an appropriate step-size value (in each Iteration), a line-search algorithm is employed for the proposed discrete-time ZNN model. Computer-simulation results substantiate the effectiveness of the proposed ZNN model aided with a line-search algorithm, in addition to the connection and explanation to Newton Iteration for matrix square root finding.

  • solution of nonlinear equations by continuous and discrete time zhang dynamics and more importantly their links to Newton Iteration
    International Conference on Information and Communication Security, 2009
    Co-Authors: Yunong Zhang, Ning Tan
    Abstract:

    Different from gradient-based dynamics (GD), a special class of neural dynamics has been found, developed, generalized and investigated by Zhang et al, e.g., for online solution of time-varying and/or static nonlinear equations. The resultant Zhang dynamics (ZD) is designed based on the elimination of an indefinite error-function (instead of the elimination of a square-based positive or at least lower-bounded energy-function usually associated with GD and/or Hopfield-type neural newtorks). In this paper, discrete-time ZD models (different from our previous research on continuous-time ZD models) are developed and investigated. In terms of nonlinear-equations solving, the Newton Iteration (also termed, Newton-Raphson Iteration) is found to be a special case of the ZD models (by focusing on the static-problem solving, utilizing the linear activation function and fixing the step-size to be 1). Noticing this new relation and explanation, we conduct computer-simulation, testing and comparisons for such discrete-time ZD models (including Newton Iteration) for nonlinear equations solving. The numerical results substantiate the theoretical analysis, explanation, unification and efficacy of the discrete-time ZD models on nonlinear equations solving.

  • from zhang neural network to Newton Iteration for matrix inversion
    IEEE Transactions on Circuits and Systems, 2009
    Co-Authors: Yunong Zhang, Binghuang Cai
    Abstract:

    Different from gradient-based neural networks, a special kind of recurrent neural network (RNN) has recently been proposed by Zhang for online matrix inversion. Such an RNN is designed based on a matrix-valued error function instead of a scalar-valued error function. In addition, it was depicted in an implicit dynamics instead of an explicit dynamics. In this paper, we develop and investigate a discrete-time model of Zhang neural network (termed as such and abbreviated as ZNN for presentation convenience), which is depicted by a system of difference equations. Comparing with Newton Iteration for matrix inversion, we find that the discrete-time ZNN model incorporates Newton Iteration as its special case. Noticing this relation, we perform numerical comparisons on different situations of using ZNN and Newton Iteration for matrix inversion. Different kinds of activation functions and different step-size values are examined for superior convergence and better stability of ZNN. Numerical examples demonstrate the efficacy of both ZNN and Newton Iteration for online matrix inversion.

  • on the variable step size of discrete time zhang neural network and Newton Iteration for constant matrix inversion
    Intelligent Information Technology Application, 2008
    Co-Authors: Yunong Zhang, Binghuang Cai, Mingjiong Liang
    Abstract:

    A special kind of recurrent neural network has recently been proposed by Zhang et al for matrix inversion. Then, for possible hardware and digital-circuit realization, the corresponding discrete-time model of Zhang neural network (ZNN) is proposed for constant matrix inversion, which reduces exactly to Newton Iteration when linear activation functions and constat step-size 1 are used. In this paper, a variable step-size choosing method is investigated for such a discrete-time ZNN model, in which different variable step-size rules are derived for different kinds of activation functions. For comparative purposes, the fixed step-size choosing method is presented as well. Numerical examples demonstrate the efficacy of the discrete-time ZNN model, especially when using the variable step-size method.

Lianfeng Shen - One of the best experts on this subject based on the ideXlab platform.

  • massive mimo pre coding algorithm based on improved Newton Iteration
    Vehicular Technology Conference, 2017
    Co-Authors: Yongqiang Man, Chi Zhang, Feng Yan, Song Xing, Lianfeng Shen
    Abstract:

    Regular zero-forcing (RZF) precoding algorithm is well- known as its low complexity and high performance in massive MIMO systems. However, when the number of transmitting antennas increases, the matrix inversion in RZF leads to high algorithmic complexity. In this paper, we propose an improved Newton Iteration to estimate the matrix inversion in RZF precoding. Compared with the traditional Newton Iteration, the performance improvement of the proposed algorithm is achieved in both of the fast algorithm convergence and the average user arrival rate in RZF precoding.

  • VTC Spring - Massive MIMO Pre-Coding Algorithm Based on Improved Newton Iteration
    2017 IEEE 85th Vehicular Technology Conference (VTC Spring), 2017
    Co-Authors: Yongqiang Man, Chi Zhang, Feng Yan, Song Xing, Lianfeng Shen
    Abstract:

    Regular zero-forcing (RZF) precoding algorithm is well- known as its low complexity and high performance in massive MIMO systems. However, when the number of transmitting antennas increases, the matrix inversion in RZF leads to high algorithmic complexity. In this paper, we propose an improved Newton Iteration to estimate the matrix inversion in RZF precoding. Compared with the traditional Newton Iteration, the performance improvement of the proposed algorithm is achieved in both of the fast algorithm convergence and the average user arrival rate in RZF precoding.

Binghuang Cai - One of the best experts on this subject based on the ideXlab platform.

  • zhang neural network and its application to Newton Iteration for matrix square root estimation
    Neural Computing and Applications, 2012
    Co-Authors: Yunong Zhang, Binghuang Cai, Yiwen Yang, Dongsheng Guo
    Abstract:

    A special class of recurrent neural networks (RNN) has recently been proposed by Zhang et al. for solving online time-varying matrix problems. Being different from conventional gradient-based neural networks (GNN), such RNN (termed specifically as Zhang neural networks, ZNN) are designed based on matrix-valued error functions, instead of scalar-valued norm-based energy functions. In this paper, we generalize and further investigate the ZNN model for time-varying matrix square root finding. For the purpose of possible hardware (e.g., digital circuit) realization, a discrete-time ZNN model is constructed and developed, which incorporates Newton Iteration as a special case. Besides, to obtain an appropriate step-size value (in each Iteration), a line-search algorithm is employed for the proposed discrete-time ZNN model. Computer-simulation results substantiate the effectiveness of the proposed ZNN model aided with a line-search algorithm, in addition to the connection and explanation to Newton Iteration for matrix square root finding.

  • from zhang neural network to Newton Iteration for matrix inversion
    IEEE Transactions on Circuits and Systems, 2009
    Co-Authors: Yunong Zhang, Binghuang Cai
    Abstract:

    Different from gradient-based neural networks, a special kind of recurrent neural network (RNN) has recently been proposed by Zhang for online matrix inversion. Such an RNN is designed based on a matrix-valued error function instead of a scalar-valued error function. In addition, it was depicted in an implicit dynamics instead of an explicit dynamics. In this paper, we develop and investigate a discrete-time model of Zhang neural network (termed as such and abbreviated as ZNN for presentation convenience), which is depicted by a system of difference equations. Comparing with Newton Iteration for matrix inversion, we find that the discrete-time ZNN model incorporates Newton Iteration as its special case. Noticing this relation, we perform numerical comparisons on different situations of using ZNN and Newton Iteration for matrix inversion. Different kinds of activation functions and different step-size values are examined for superior convergence and better stability of ZNN. Numerical examples demonstrate the efficacy of both ZNN and Newton Iteration for online matrix inversion.

  • on the variable step size of discrete time zhang neural network and Newton Iteration for constant matrix inversion
    Intelligent Information Technology Application, 2008
    Co-Authors: Yunong Zhang, Binghuang Cai, Mingjiong Liang
    Abstract:

    A special kind of recurrent neural network has recently been proposed by Zhang et al for matrix inversion. Then, for possible hardware and digital-circuit realization, the corresponding discrete-time model of Zhang neural network (ZNN) is proposed for constant matrix inversion, which reduces exactly to Newton Iteration when linear activation functions and constat step-size 1 are used. In this paper, a variable step-size choosing method is investigated for such a discrete-time ZNN model, in which different variable step-size rules are derived for different kinds of activation functions. For comparative purposes, the fixed step-size choosing method is presented as well. Numerical examples demonstrate the efficacy of the discrete-time ZNN model, especially when using the variable step-size method.

Yongqiang Man - One of the best experts on this subject based on the ideXlab platform.

  • massive mimo pre coding algorithm based on improved Newton Iteration
    Vehicular Technology Conference, 2017
    Co-Authors: Yongqiang Man, Chi Zhang, Feng Yan, Song Xing, Lianfeng Shen
    Abstract:

    Regular zero-forcing (RZF) precoding algorithm is well- known as its low complexity and high performance in massive MIMO systems. However, when the number of transmitting antennas increases, the matrix inversion in RZF leads to high algorithmic complexity. In this paper, we propose an improved Newton Iteration to estimate the matrix inversion in RZF precoding. Compared with the traditional Newton Iteration, the performance improvement of the proposed algorithm is achieved in both of the fast algorithm convergence and the average user arrival rate in RZF precoding.

  • VTC Spring - Massive MIMO Pre-Coding Algorithm Based on Improved Newton Iteration
    2017 IEEE 85th Vehicular Technology Conference (VTC Spring), 2017
    Co-Authors: Yongqiang Man, Chi Zhang, Feng Yan, Song Xing, Lianfeng Shen
    Abstract:

    Regular zero-forcing (RZF) precoding algorithm is well- known as its low complexity and high performance in massive MIMO systems. However, when the number of transmitting antennas increases, the matrix inversion in RZF leads to high algorithmic complexity. In this paper, we propose an improved Newton Iteration to estimate the matrix inversion in RZF precoding. Compared with the traditional Newton Iteration, the performance improvement of the proposed algorithm is achieved in both of the fast algorithm convergence and the average user arrival rate in RZF precoding.

Song Xing - One of the best experts on this subject based on the ideXlab platform.

  • massive mimo pre coding algorithm based on improved Newton Iteration
    Vehicular Technology Conference, 2017
    Co-Authors: Yongqiang Man, Chi Zhang, Feng Yan, Song Xing, Lianfeng Shen
    Abstract:

    Regular zero-forcing (RZF) precoding algorithm is well- known as its low complexity and high performance in massive MIMO systems. However, when the number of transmitting antennas increases, the matrix inversion in RZF leads to high algorithmic complexity. In this paper, we propose an improved Newton Iteration to estimate the matrix inversion in RZF precoding. Compared with the traditional Newton Iteration, the performance improvement of the proposed algorithm is achieved in both of the fast algorithm convergence and the average user arrival rate in RZF precoding.

  • VTC Spring - Massive MIMO Pre-Coding Algorithm Based on Improved Newton Iteration
    2017 IEEE 85th Vehicular Technology Conference (VTC Spring), 2017
    Co-Authors: Yongqiang Man, Chi Zhang, Feng Yan, Song Xing, Lianfeng Shen
    Abstract:

    Regular zero-forcing (RZF) precoding algorithm is well- known as its low complexity and high performance in massive MIMO systems. However, when the number of transmitting antennas increases, the matrix inversion in RZF leads to high algorithmic complexity. In this paper, we propose an improved Newton Iteration to estimate the matrix inversion in RZF precoding. Compared with the traditional Newton Iteration, the performance improvement of the proposed algorithm is achieved in both of the fast algorithm convergence and the average user arrival rate in RZF precoding.