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首页> 外文会议>International Conference on Intelligent Control and Information Processing >Nonlinearly Activated Complex-Valued Gradient Neural Network for Complex Matrix Inversion
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Nonlinearly Activated Complex-Valued Gradient Neural Network for Complex Matrix Inversion

机译：用于复杂矩阵求逆的非线性激活复数值梯度神经网络

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摘要

A new nonlinear activated complex-valued gradient neural network is proposed, which is used for complex matrix inversion in the complex area. This work focuses on the inversion of complex-valued matrices in complex domains rather than finding the inverse of real-valued matrices. Compared with the traditional linear complex gradient neural network (GNN), the main contribution of this paper introduces a nonlinear activation function, which can effectively improve the convergence rate of the GNN model. The computer simulation substantiate the effectiveness and superiority of nonlinearly activated complex-valued gradient neural network (NACVGNN) to the matrix inversion, as compared with the linear complex gradient neural network.
机译：提出了一种新的非线性激活复数值梯度神经网络，用于复杂区域的复杂矩阵反演。这项工作着重于复杂域中复杂值矩阵的求逆，而不是寻找实值矩阵的逆。与传统的线性复杂梯度神经网络（GNN）相比，本文的主要贡献是引入了非线性激活函数，可以有效提高GNN模型的收敛速度。与线性复杂梯度神经网络相比，计算机仿真证实了非线性激活的复合值梯度神经网络（NACVGNN）相对于矩阵求逆的有效性和优越性。

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