This paper introduces a novel fusion neural architecture and the use of a novel Lyapunov theory-based algorithm, for the online approximation of the dynamics of nonlinear systems. The proposed neural system, in combination with the proposed update rule of the neural weights, achieves fast convergence of the identification process, ensuring at the same time stability of the error system in the sense of Lyapunov theory. The fusion neural system combines the features that are extracted from two-independent neural streams, a feedforward and a diagonal recurrent one, satisfying different design criteria of the identification task. Simulation results for five cases reveal the approximation strength of both proposed fusion neural architecture and proposed learning algorithm. Also, additional experiments demonstrate the effectiveness in cases of parameter variations and additive noise.

中文翻译：

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基于 Lyapunov 理论的融合神经网络识别动态非线性系统

本文介绍了一种新颖的融合神经架构和一种新颖的基于 Lyapunov 理论的算法，用于非线性系统动力学的在线近似。所提出的神经系统结合所提出的神经权重更新规则，实现了识别过程的快速收敛，同时保证了李雅普诺夫理论意义上的误差系统的稳定性。融合神经系统结合了从两个独立的神经流中提取的特征，一个前馈和一个对角循环的，满足识别任务的不同设计标准。五个案例的模拟结果揭示了所提出的融合神经架构和所提出的学习算法的逼近强度。还，