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张雨浓, Y. Zhang and K. Chen
ELECTRONICS LETTERS 17th January 2008 Vol. 44 No. 2,-0001,():
-1年11月30日
The Wang neural network, together with its improved circuit implementation, could solve online a set of simultaneous linear equations. Global exponential convergence is presented for the Wang neural network, compared to the previously-presented asymptotical convergence. In addition, global stability results are presented for the Wang neural network. Illustrative examples further demonstrate the characteristics of the Wang neural network.
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张雨浓, 马伟木, 李克讷, 易称福
中国科技信息,20085,(13),-0001,():
-1年11月30日
简述了协处理器的概念、任务、发展历程和现状,探讨了协处理器之所以引起人们重视和再重视的原因及其优势,简单介绍和展望了如何用FPGA等类型协处理器构建高性能计算平台。
协处理器, 专用处理芯片, 浮点单元, 高性能计算, 现场可编程门阵列
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张雨浓, Yunong Zhang *
Neurocomputing 70(2006)513-524,-0001,():
-1年11月30日
In this paper, for handling general minimum-effort inverse-kinematic problems, the nonuniqueness condition is investigated. A set of nonlinear equations and inequality is presented for online nonuniqueness-checking. The concept and utility of primal neural networks (NNs) are introduced in this context of dynamical inequalities and constraints. The proposed primal NN can handle well such a nonlinear online-checking problem in the form of a set of nonlinear equations and inequality. Numerical examples demonstrate the effectiveness and advantages of the primal NN approach.
Minimum effort inverse kinematics, Nonuniqueness, Discontinuity, Nonlinear equations and inequalities, Primal neural network
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【期刊论文】梯度神经网络求解Sylvester方程之MATLAB仿真
张雨浓, 杨逸文, 陈轲, 蔡炳煌
系统仿真学报,2009,21(13): 4028~4031、4037,-0001,():
-1年11月30日
近年来,国内外学者发表了许多关于线性代数问题实时求解的方法,其中包括了矩阵求逆和线性方程组的并行求解方法。在研究了基于梯度法的递归神经网络用于Sylvester矩阵方程的实时求解后,通过使用Kronecker乘积和矩阵向量化等技术进行了MATLAB仿真从而验证了相关理论分析。计算机仿真的结果证实了这类神经网络方法在解决Sylvester矩阵方程中的有效性和高效率(特别是在使用幂S型激励函数的情况下)。
基于梯度法的递归神经网络, Sylvester方程, Kronecker乘积, 向量化, MATLAB 仿真
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【期刊论文】Convergence Properties Analysis of Gradient Neurak Network for Solving Online Linear Equations
张雨浓, ZHANG Yu-Nong, CHEN Zeng-Hai, CHEN Ke
ACTA AUTOMATICA SINICA Vol.35, No. 8 Aygust, 2009,-0001,():
-1年11月30日
A gradient neural network (CNN) for solving online a set of simultancous linsr equations is gencralized and investigated in this paper. Instead if the carlier-prcesented asymptotical convergence, global exponontial convergence could be proved for such a class of ncural networks, In addition, superior convergcnce could be achicved using power-sigmoid avtivation-funcitionsm, compared with tiing lincar activation-functions Computer-simulation results substantiate further the above analysis and efficacy of such neural network.
Gradincnt neural network (, GNN), ,, activation-function array,, global exponential convergence
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