With a view to the interference of piecewise constant arguments (PCAs) and neutral terms (NTs) to the original system and the significant applications in the signal transmission process, we explore the robustness of the exponentially global stability (EGS) of recurrent neural network (RNN) with PCAs and NTs (NPRNN). The following challenges arise: what the range of PCAs and the scope of NTs can NPRNN tolerate to be exponentially stable. So we derive two important indicators: maximum interval length of PCAs and the scope of neutral term (NT) compression coefficient here for NPRNN to be exponentially stable. Additionally, we theoretically proved that if the interval length of PCAs and the bound of NT compression coefficient are all lower than the given results herein, the disturbed NPRNN will still remain global exponential stability. Finally, there are two numerical examples to verify the deduced results’ effectiveness here.

中文翻译：

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具有中性项和广义分段常数参数的循环神经网络指数全局稳定性的鲁棒性探索

鉴于分段常数参数（PCA）和中性项（NTs）对原始系统的干扰以及在信号传输过程中的重要应用，我们探索了循环神经网络指数全局稳定性（EGS）的鲁棒性（ RNN) 与 PCA 和 NTs (NPRNN)。出现了以下挑战： NPRNN 可以容忍的 PCA 范围和 NT 范围是指数稳定的。因此我们推导出两个重要指标：PCA 的最大间隔长度和 NPRNN 指数稳定的中性项（NT）压缩系数范围。此外，我们从理论上证明，如果 PCA 的间隔长度和 NT 压缩系数的界限都低于这里给定的结果，则受扰的 NPRNN 仍将保持全局指数稳定性。最后，