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首页> 外文期刊>IEEE Transactions on Systems, Man, and Cybernetics >Solving Time-Varying System of Nonlinear Equations by Finite-Time Recurrent Neural Networks With Application to Motion Tracking of Robot Manipulators
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Solving Time-Varying System of Nonlinear Equations by Finite-Time Recurrent Neural Networks With Application to Motion Tracking of Robot Manipulators

机译：有限时间递归神经网络求解非线性方程的时变系统及其在机器人操纵器运动跟踪中的应用

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摘要

Two novel nonlinearly activated recurrent neural networks (RNNs) with finite-time convergence [called finite-time RNNs (FTRNNs)] are proposed and analyzed to solve efficiently time-varying systems of nonlinear equations (SoNEs). Compared with previously presented neural networks for solving such a SoNE, the FTRNNs are activated by new nonlinear activation functions and thus possess a better finite-time convergence property. In addition, theoretical analyses about FTRNNs are presented to determine the upper bounds of convergence time under the context of using such two novel nonlinear activation functions. Computer simulations based on a numerical example validate the preponderance of the proposed FTRNNs for time-varying SoNE, as compared to the recently proposed Zhang neural network and its improved version. Finally, an engineering practical example to motion tracking of a robot manipulator demonstrates the feasibility and applicability of the FTRNNs.
机译：提出并分析了两种新颖的具有有限时间收敛性的非线性激活递归神经网络（RNN）[称为有限时间RNN（FTRNN）]，以有效地求解非线性方程组（SoNE）的时变系统。与先前提出的用于解决此类SoNE的神经网络相比，FTRNN被新的非线性激活函数激活，因此具有更好的有限时间收敛性。此外，提出了有关FTRNNs的理论分析，以确定在使用这两种新颖的非线性激活函数的情况下收敛时间的上限。与最近提出的张神经网络及其改进版本相比，基于数值示例的计算机仿真验证了所提出的FTRNN对于时变SoNE的优势。最后，一个机器人机械手运动跟踪的工程实例证明了FTRNN的可行性和适用性。

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