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一种基于积分增强的递归神经网络用于求解时变等式与不等式方程组

A recurrent neural network based on integral enhancement for solving time-varying equations and inequality systems

  • 摘要: 提出了一种新颖的基于积分增强的递归神经网络以实现对时变等式与不等式方程组的求解。首先引入一组非负松弛变量,将时变不等式组转化为矩阵方程的形式,并构造出关于误差函数的矩阵微分方程。其次通过矩阵的伪逆变换,推导出该矩阵微分方程的显式解,并在此基础上加入积分增强项,以提高求解模型的抗干扰能力。最后的计算仿真结果验证了所提出的积分增强递归神经网络的有效性和优越性。

     

    Abstract: In the report, a novel recurrent neural network based on integral enhancement to solve time-varying systems of equations and inequations was proposed. Firstly, a set of non-negative relaxation variables was introduced, the time-varying inequality system was transformed into the form of a matrix equation, and a matrix differential equation about the error function was constructed; secondly, the inverse transformation of the matrix was used to derive the explicit solution of the matrix differential equation, and on which an integral enhancement term was added to improve the anti-interference ability of the solving model; lastly, the final computational simulation results validated the effectiveness and superiority of the proposed integral enhanced recurrent neural network.

     

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