This article mainly studies the iterative parameter estimation problems of a class of nonlinear systems. Based on the auxiliary model identification idea, this article utilizes the estimated parameters to construct an auxiliary model, and uses its outputs to replace the unknown noise-free process outputs, and develops an auxiliary model least squares-based iterative (AM-LSI) identification algorithm. For further improving the parameter estimation accuracy, we use a particle filter to estimate the unknown noise-free process outputs, and derive a particle filtering least squares-based iterative (PF-LSI) identification algorithm. During each iteration, the AM-LSI and PF-LSI algorithms can make full use of the measured input–output data. The simulation results indicate that the proposed algorithms are effective for identifying the nonlinear systems, and can generate more accurate parameter estimates than the auxiliary model-based recursive least squares algorithm.

中文翻译：

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基于粒子滤波技术的一类双线性系统迭代辨识方法

本文主要研究一类非线性系统的迭代参数估计问题。本文基于辅助模型识别思想，利用估计参数构建辅助模型，并用其输出替换未知的无噪声过程输出，开发了基于最小二乘法（AM-LSI）识别的辅助模型算法。为了进一步提高参数估计精度，我们使用粒子滤波器来估计未知的无噪声过程输出，并推导出基于粒子滤波最小二乘迭代（PF-LSI）的识别算法。在每次迭代中，AM-LSI 和 PF-LSI 算法可以充分利用测量的输入-输出数据。仿真结果表明，所提出的算法对于识别非线性系统是有效的，