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Analog Computing for Real-Time Solution of Time-Varying Linear Equations

机译:时变线性方程组实时解的模拟计算

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An implicit recurrent neural network model (IRNN) is proposed in this paper for solving on-line time-varying linear equations. Such a neural network can be implemented as analog circuits or VLSI. Excellent convergent properties have been revealed by careful theoretical analysis. In the specific case where the linear equation is obtained from a time-varying Sylvester equation, the proposed IRNN model coincides with some existing recurrent neural networks reported in recent literature, where simulation examples have been reported to demonstrate the effectiveness and efficiency.
机译:提出了一种隐式递归神经网络模型(IRNN),用于求解在线时变线性方程组。这样的神经网络可以被实现为模拟电路或VLSI。仔细的理论分析显示出了出色的收敛性。在从时变Sylvester方程获得线性方程的特定情况下,所提出的IRNN模型与最近文献中报道的一些现有的递归神经网络相吻合,其中已报道了仿真示例以证明其有效性和效率。

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