On account of the rapid progress of zeroing neural network (ZNN) and the extensive use of fuzzy logic system (FLS), this article proposes an intelligent fuzzy robustness ZNN (IFR-ZNN) model and applies it to solving the time-variant Stein matrix equation (TVSME) problem. Be different from ZNN models before, the IFR-ZNN model uses a fuzzy parameter as the design parameter and adopts a first proposed improved nonlinear piecewise activation function. Particularly, the FLS that generates the fuzzy parameter utilizes an improved membership function of nonuniform distribution which can improve the adaptability and robustness of the IFR-ZNN model. Based on the above two optimizations, the proposed IFR-ZNN model possesses three significant advantages: (1) fixed-time convergence independent of initial states; (2) superior robustness to tolerate two kinds of noises simultaneously; and (3) better adaptiveness based on computational error. Besides, the upper bounds of fixed-time convergence of the IFR-ZNN model under noisy or non-noisy situations are calculated theoretically, and the stability as well as the excellent adaptability are analyzed in detail. Finally, simulation comparison results manifest the availability and meliority of the proposed IFR-ZNN model in solving the TVSME problem.

中文翻译：

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时变Stein矩阵方程定时收敛的智能模糊鲁棒ZNN模型

鉴于归零神经网络（ZNN）的快速发展和模糊逻辑系统（FLS）的广泛使用，本文提出了一种智能模糊鲁棒性ZNN（IFR-ZNN）模型，并将其应用于求解时变Stein矩阵方程（TVSME）问题。与以往的ZNN模型不同，IFR-ZNN模型以模糊参数作为设计参数，采用了首次提出的改进非线性分段激活函数。特别地，生成模糊参数的FLS利用改进的非均匀分布隶属函数，可以提高IFR-ZNN模型的适应性和鲁棒性。基于以上两个优化，所提出的 IFR-ZNN 模型具有三个显着优势：（1）独立于初始状态的固定时间收敛；(2) 超强的鲁棒性，可以同时容忍两种噪声；(3) 基于计算误差的更好适应性。此外，从理论上计算了IFR-ZNN模型在噪声或非噪声情况下的固定时间收敛上限，并详细分析了稳定性和良好的适应性。最后，仿真比较结果表明了所提出的 IFR-ZNN 模型在解决 TVSME 问题中的可用性和优越性。