Time-varying dynamic system contaminated by cognitive noises is universal in the fields of engineering and science. In this article, a circadian rhythms learning network (CRLN) is proposed and investigated for disposing the noise disturbed time-varying dynamic system. To do so, a vector-error function is first defined. Second, a neural dynamic model is formulated. Third, a co-state matrix is integrated into the model, of which the states are the linear combination of the previous periodic states and errors, which can effectively suppress periodic noises. Theoretical analysis and mathematical derivation prove the global exponential convergence performance of the proposed CRLN model. Finally, a practical noise disturbed time-varying dynamic system example with four different noises illustrates the accuracy and efficacy of the proposed CRLN model. Comparisons with traditional zeroing neural network further verify the advantages of the proposed CRLN model.

中文翻译：

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用于抵抗时变动态系统的认知周期噪声的昼夜节律学习网络及其在机器人中的应用

被认知噪声污染的时变动态系统在工程和科学领域普遍存在。在本文中，提出并研究了昼夜节律学习网络（CRLN）来处理噪声干扰的时变动态系统。为此，首先定义向量误差函数。其次，制定了神经动力学模型。第三，在模型中集成了一个共态矩阵，其状态是先前周期状态和误差的线性组合，可以有效抑制周期噪声。理论分析和数学推导证明了所提出的CRLN模型的全局指数收敛性能。最后，具有四种不同噪声的实际噪声干扰时变动态系统示例说明了所提出的 CRLN 模型的准确性和有效性。