This article is concerned with the parameter identification of output‐error bilinear‐parameter models with colored noises from measurement data. An auxiliary model least squares‐based iterative method is developed through the overparameterization model. It examines the difficulty of estimating the overparameterized vector, which usually presents a heavy computational burden in the identification process. To overcome this drawback, a parameter separation technique is introduced and the nonlinear model is reformulated as a refined identification model through eliminating the crossmultiplying terms. In this regard, a parameter separation least squares‐based iterative (PS‐LSI) algorithm is derived by avoiding estimating the redundant parameters. On the basis of the PS‐LSI algorithm, we derive a maximum likelihood least squares‐based iterative method to further improve the numerical accuracy. The identification is dependent on the formulation of a pseudolinear regression relationship, which contains two linear prefilters constructed from the system and noise models. The performance of this proposed method is confirmed by the numerical simulations as well as direct comparisons with other existing algorithms.

中文翻译：

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具有色噪声的输出误差双线性参数模型的基于最大似然最小二乘的迭代方法

本文涉及带有测量数据中有色噪声的输出误差双线性参数模型的参数识别。通过超参数化模型开发了一种基于最小二乘的辅助模型迭代方法。它检查了估计过参数化向量的困难，这通常会在识别过程中带来沉重的计算负担。为了克服这个缺点，引入了参数分离技术，并且通过消除交叉乘法项，将非线性模型重新构造为精细的识别模型。在这方面，通过避免估计冗余参数来推导参数分离最小二乘迭代（PS-LSI）算法。根据PS‐LSI算法，我们推导了基于最大似然最小二乘的迭代方法，以进一步提高数值精度。识别取决于拟线性回归关系的公式，其中包含两个由系统和噪声模型构成的线性预滤波器。数值模拟以及与其他现有算法的直接比较证实了该方法的性能。