门控循环神经网络（Gated RNNs）因其高精度而广泛应用于非线性系统辨识，尽管其常表现出复杂且难以分析的混沌动态。本文研究了无混沌网络（CFN）的系统理论特性，该架构最初提出旨在消除标准门控RNN中的混沌行为。首先，我们形式化证明了CFN在设计上满足输入-状态稳定性（ISS）。然而，我们表明确保增量输入-状态稳定性（delta-ISS）仍需对CFN架构施加特定参数约束。为此，我们提出了解耦门网络（DGN），一种CFN的新结构变体，通过在门控机制中移除内部状态连接来实现改进。最后，我们证明DGN无条件满足delta-ISS性质，为非线性动力系统辨识提供了一个增量稳定架构，无需复杂的网络训练调整。数值结果证实，DGN在保持标准架构建模能力的同时，严格遵循这些严格的稳定性保证。

Gated Recurrent Neural Networks (RNNs) are widely used for nonlinear system identification due to their high accuracy, although they often exhibit complex, chaotic dynamics that are difficult to analyze. This paper investigates the system-theoretic properties of the Chaos-Free Network (CFN), an architecture originally proposed to eliminate the chaotic behavior found in standard gated RNNs. First, we formally prove that the CFN satisfies Input-to-State Stability (ISS) by design. However, we demonstrate that ensuring Incremental ISS (delta-ISS) still requires specific parametric constraints on the CFN architecture. Then, to address this, we introduce the Decoupled-Gate Network (DGN), a novel structural variant of the CFN that removes internal state connections in the gating mechanisms. Finally, we prove that the DGN unconditionally satisfies the delta-ISS property, providing an incrementally stable architecture for identifying nonlinear dynamical systems without requiring complex network training modifications. Numerical results confirm that the DGN maintains the modeling capabilities of standard architectures while adhering to these rigorous stability guarantees.