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.

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。

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.

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）。

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.