Identification of Hammerstein-Wiener ARMAX Systems Using Extended Kalman Filter

摘要：

In this study, Extended Kalman Filter (EKF) algorithm is developed to estimate the parameters of Hammerstein-Wiener (H-W) ARMAX models. The basic idea is to estimate the original parameters of the identification model, which are appeared in the form of product terms, directly. While, other algorithms like Extended Forgetting Factor Stochastic Gradient (EFG), Extended Stochastic Gradient (ESG), Forgetting Factor Recursive Least Square (FFRLS) and Kalman Filter (KF), estimate parameters in the product form and they need another algorithms such averaging method (AVE method), singular value decomposition method (SVD method) to separate the parameters. So, the computational complexity of the proposed approach decreases. To show the efficiency of this method the results are compared with EFG and ESG method.

关键词： Hammerstein-Wiener model Extended Kalman Filter algorithm Extended Stochastic Gradient algorithm Extended Forgetting Factor Stochastic Gradient algorithm

作者: M. Mansouri Tolouei M. Aliyari Shoorehdeli

作者单位: Intelligent System Laboratory (ISLAB), faculty of Electrical Engineering, Control department, K.N. T Advanced Process Automation & Control Laboratory (APAC), faculty of Electrical Engineering, Control Advanced Process Automation & Control Laboratory (APAC), faculty of Electrical Engineering, Mechatro

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 1110-1114

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）