Recurrent neural networks (RNNs) have found a great variety of application areas. As a special type of RNNs, zeroing neural network (ZNN), or termed Zhang neural network, has been reported to have powerful abilities to address various nonstationary problems. To overcome drawbacks and improve the performance of existing ZNN models, several modified ZNN models are proposed in this paper, which allow nonconvex activation functions and possess accelerated finite-time convergence property. Theoretical analyses suggest that the developed ZNN models are equipped with the global convergence property and the convergence-accelerated models are verified by the estimated upper bounds of convergence time. Finally, comparative and illustrative simulation results, including a verification on a mobile manipulator, are presented to illustrate the effectiveness and superiority of proposed ZNN models to existing models for solving nonstationary Sylvester equations.

中文翻译：

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用于求解非平稳Sylvester方程的新型调零神经网络模型，并在移动机械手上进行了验证

递归神经网络（RNN）已经发现了各种各样的应用领域。作为一种特殊的RNN，调零神经网络（ZNN）称为张神经网络，具有解决各种非平稳问题的强大能力。为了克服现有的ZNN模型的缺点并提高其性能，本文提出了几种改进的ZNN模型，它们具有非凸激活函数并具有加速的有限时间收敛性。理论分析表明，所开发的ZNN模型具有全局收敛性，并且通过估计的收敛时间上限验证了收敛加速模型。最后，比较和说明性的仿真结果，包括在移动操纵器上的验证，