A Parameter-Changing and Complex-Valued Zeroing Neural-Network for Finding Solution of Time-Varying Complex Linear Matrix Equations in Finite Time
A Parameter-Changing and Complex-Valued Zeroing Neural-Network for Finding Solution of Time-Varying Complex Linear Matrix Equations in Finite Time
复制标题
求解有限时间内时变复线性矩阵方程的参数变化复值归零神经网络
DOI：
10.1109/tii.2021.3049413
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发表时间：
2021-01
影响因子：
12.3
通讯作者：
He Yongjun
中科院分区：
文献类型：
--
作者：
Xiao Lin;Tao Juan;Dai Jianhua;Wang Yaonan;Jia Lei;He Yongjun
为了求解复域上的复值时变系数线性矩阵方程组（CV-LME-TVC），通过引入一种新的变参数函数，提出了一种变参数复值调零神经网络（PC-CVZNN）模型。与已有的参数固定的复值调零神经网络（CVZNN）相比，PC-CVZNN模型由于新的参数变化函数的加速作用而具有上级性能.在理论分析部分，我们利用李雅普诺夫方法证明了所提出的PC-CVZNN模型在采用线性激励函数时具有全局和超指数收敛性，在采用新的符号双幂激励函数及其修正函数时具有超有限时间收敛性。通过部分数值对比实验表明，PC-CVZNN模型在求解CV-LME-TVC问题时，比固定参数CVZNN模型和其他具有参数变化函数的模拟神经网络具有更快的收敛速度。重要的是，所提出的方法的应用程序的移动的机械手控制提供了潜在的实用价值的PC-CVZNN模型在工业领域。
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.