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An Anti-Disturbance Integral Recursive Neural Network for Solving Time-Varying Matrix Inversion

机译:一种用于解决时变矩阵反转的防扰积分递归神经网络

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To solve the time-varying matrix for inverse problem, an anti-disturbance integral recursive neural network (ADIRNN) is propounded in this study. The AD-IRNN method with different activation functions is exploited and analyzed, which proves that the proposed method is global convergent. Furthermore, because of the integral-type design formula, the ADIRNN method has strong robustness and can resist disturbances effectively. By incorporating an integral term, the norm-based error function of the AD-IRNN method can converge to zero, and the convergence rate is faster than that of the zeroing neural network. Comparative experiments verify that the AD-IRNN method has the superiority of solving the time-varying matrix for inverse problem.
机译:为了解决逆问题的时变矩阵,本研究中取出了抗扰动积分递归神经网络(Adirnn)。 利用和分析具有不同激活功能的AD-IrNN方法,证明了所提出的方法是全球收敛。 此外,由于整体式设计式公式,Adirnn方法具有强大的鲁棒性,并且可以有效地抵抗干扰。 通过结合积分项,AD-IRNN方法的基于规范的误差函数可以收敛到零,并且收敛速率比归零神经网络的速度快。 比较实验验证AD-IRNN方法是否具有解决逆问题的时变矩阵的优越性。

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