This article is concerned with the parameter identification problem of nonlinear dynamic responses for the linear time‐invariant system by means of an impulse excitation signal and discrete observation data. Using the impulse signal as the input, the impulse response experiment is carried out and the dynamical moving sampling is designed to generate the measured data for deriving new identification algorithms. By applying the moving window data that contain the dynamical information of the system to be identified, an objective function with respect to the parameters of the systems is constructed according to the impulse response. In accordance with different functional relations between the system parameters and the system output response, the unknown parameter vector of the system is separated into a linear parameter vector and a nonlinear parameter vector. Based on the separated parameter vectors, two subidentification models are constructed and a separable identification algorithm is presented through the gradient search to improve the accuracy. Moreover, for the purpose of enhancing the estimation accuracy and capturing the dynamical feature of the systems, the moving window data are employed to develop the separable identification algorithm. The performance of the proposed separable identification method is illustrated via a numerical example.

中文翻译：

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系统非线性动力响应的可分多次创新随机梯度估计算法

本文关注的是利用脉冲激励信号和离散观测数据对线性时不变系统的非线性动力响应进行参数辨识的问题。以脉冲信号作为输入，进行了脉冲响应实验，并设计了动态移动采样以生成测量数据，以推导新的识别算法。通过应用包含将被识别的系统的动态信息的移动窗口数据，根据脉冲响应来构造关于系统参数的目标函数。根据系统参数与系统输出响应之间的不同功能关系，系统的未知参数向量被分为线性参数向量和非线性参数向量。基于分离的参数向量，构造了两个子辨识模型，并通过梯度搜索提出了一种可分离的辨识算法，以提高精度。此外，为了提高估计精度并捕获系统的动态特征，采用了移动窗口数据来开发可分离的识别算法。通过数值例子说明了所提出的可分离识别方法的性能。为了提高估计精度和捕获系统的动态特征，采用了移动窗口数据来开发可分离的识别算法。通过数值例子说明了所提出的可分离识别方法的性能。为了提高估计精度和捕获系统的动态特征，采用了移动窗口数据来开发可分离的识别算法。通过数值例子说明了所提出的可分离识别方法的性能。