This article considers the identification problems of multivariable input nonlinear systems with unmeasured disturbances. For the identification difficulty caused by the crossproducts between the parameters of the linear block and the nonlinear block, the key term separation technique is adopted to separate the parameters of the nonlinear block from the parameters of the linear block. By combining the model decomposition technique and the hierarchical identification principle, a key term separation‐based maximum likelihood recursive extended stochastic gradient algorithm with reduced computational complexity is presented to estimate all the parameters directly. By introducing the multiinnovation identification theory, a key term separation‐based maximum likelihood multiinnovation extended stochastic gradient algorithm is proposed to improve the parameter estimation accuracy. The simulation results illustrate the effectiveness of the proposed methods.

中文翻译：

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基于多元创新识别理论的多变量非线性系统最大似然梯度估计

本文考虑具有不可测扰动的多变量输入非线性系统的辨识问题。针对线性块与非线性块参数之间积积的识别困难，采用关键项分离技术将非线性块的参数与线性块的参数分离。通过结合模型分解技术和层次识别原理，提出了一种基于关键词分离的最大似然递归扩展随机梯度算法，具有较低的计算复杂度，可以直接估计所有参数。通过介绍多元创新识别理论，为了提高参数估计的准确性，提出了一种基于关键词分离的最大似然多创新扩展随机梯度算法。仿真结果说明了所提方法的有效性。