For solving complex-valued linear matrix equations with time-varying coefficients (CV-LME-TVC) in the complex field, this article proposes a parameter-changing and complex-valued zeroing neural network (PC-CVZNN) model through integrating a new parameter-changing function. As compared to previous complex-valued zeroing neural networks (CVZNNs) with fixed parameters and existing parameter-changing functions, the PC-CVZNN model can achieve superior performance due to the accelerated role of the new parameter-changing function. In parts of theoretical analysis, we take advantage of Lyapunov methodology to prove that the proposed PC-CVZNN model can acquire the global and super-exponential convergence when the linear activation function is adopted, and even acquire super finite-time convergence when the new sign-bi-power activation function and its modified one are used. In parts of numerical comparison experiments, it is shown that the PC-CVZNN model possesses faster convergence rate than fixed-parameter CVZNN models and other analogy neural networks with parameter-changing function, when applied to finding the solution of CV-LME-TVC. Importantly, an application of the proposed method to the mobile manipulator control provides the potential practical value of the PC-CVZNN model in the industrial field.

中文翻译：

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有限时间内求解时变复线性矩阵方程的参数变化复值归零神经网络


针对复域中时变系数复值线性矩阵方程(CV-LME-TVC)的求解，本文通过集成新的参数，提出了一种变参数复值归零神经网络(PC-CVZNN)模型-改变功能。与之前具有固定参数和现有变参函数的复值归零神经网络（CVZNN）相比，由于新的变参函数的加速作用，PC-CVZNN模型可以获得优越的性能。在理论分析部分，我们利用Lyapunov方法证明了所提出的PC-CVZNN模型在采用线性激活函数时能够获得全局超指数收敛，甚至在新符号时获得超有限时间收敛-使用双功率激活函数及其改进函数。部分数值对比实验表明，PC-CVZNN模型应用于求解CV-LME-TVC时，比固定参数CVZNN模型和其他具有变参功能的类比神经网络具有更快的收敛速度。重要的是，该方法在移动机械手控制中的应用为PC-CVZNN模型在工业领域提供了潜在的实用价值。