引用本文:方甜莲,贾立.含有色噪声的神经模糊Hammerstein模型分离辨识[J].控制理论与应用,2016,33(1):23~31.[点击复制]
FANG Tian-lian,JIA Li.Separation identification of neuro-fuzzy Hammerstein model with colored noise[J].Control Theory and Technology,2016,33(1):23~31.[点击复制]
含有色噪声的神经模糊Hammerstein模型分离辨识
Separation identification of neuro-fuzzy Hammerstein model with colored noise
摘要点击 2738  全文点击 1495  投稿时间：2015-05-12  修订日期：2015-07-30
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DOI编号  10.7641/CTA.2015.50390
  2016,33(1):23-31
中文关键词  非线性系统  Hammerstein模型  多信号源  增广递推最小二乘算法  神经模糊模型
英文关键词  nonlinear system  Hammerstein model  multi-signal sources  recursive extended least squares algorithm  neuro-fuzzy model
基金项目  国家自然科学基金项目(61374044), 上海市教委创新重点项目(14ZZ088), 2013年上海市人才发展基金, 上海市宝山区科学技术委员会项目 (bkw2013120)资助.
作者单位邮编
方甜莲 上海大学 机电工程与自动化学院 自动化系 200072
贾立* 上海大学 机电工程与自动化学院 自动化系 200072
中文摘要
      针对实际工业过程中普遍存在的有色噪声, 本文提出一种基于递推增广最小二乘算法的神经模糊Hammerstein 模型辨识方法, 突破了传统的Hammerstein模型迭代分离算法. 首先, 利用多信号源实现Hammerstein模型中 静态非线性环节和动态线性环节的分离, 大大简化了辨识过程, 提高了串联环节参数的分离精度. 其次, 利用长除法 将噪声模型用有限脉冲响应模型逼近, 采用增广递推最小二乘法进行线性环节的参数估计. 最后, 采用神经模糊模 型拟合静态非线性环节, 同时设计了神经模糊模型参数的非迭代优化算法, 改善了模型的使用范围. 该方法保证了 模型的预测精度, 对含有色噪声的非线性系统具有较好的拟合效果. 仿真结果验证了上述方法的有效性.
英文摘要
      To deal with the colored noises commonly existing in practical industrial processes, we propose an identification method for neuro-fuzzy Hammerstein model using the recursive extended least squares algorithm (RELS). This method is different from the traditional iterative separation methods for identifying Hammerstein model. Firstly, multiple signal sources are employed to separate the static nonlinear part and the dynamic linear part of the Hammerstein model in order to simplify the identification process and improve the accuracy of the model parameters. Secondly, the finite impulse response model is used to approximate the colored noise model by using the long division method. Then, parameters of the linear part are estimated by using the RELS algorithm. Finally, the neuro-fuzzy model is adopted in identifying the static nonlinear part. Meanwhile, a noniterative neuro-fuzzy optimization algorithm which can be applied to many nonlinear systems is designed. The proposed method can guarantee high precision of the Hammerstein model. Moreover, it has the ability to approximate the nonlinear systems with colored noises. Simulation results show the effectiveness of the proposed method.