Interactive Identification Method for Box-Jenkins Models

摘要：

This paper converts a Box-Jenkins model into two identification submodels with the system model parameters and the noise model parameters, respectively. However, the information vectors in the submodels contain unmeasurable variables, which leads the conventional recursive least squares algorithm impossible to generate the parameter estimates. In order to overcome this difficulty, the interactive least squares algorithm is derived by using the auxiliary model identification idea and the hierarchical identification principle. The simulation results indicate that the proposed algorithm has less computational burden and more accurate parameter estimation compared with the auxiliary model based recursive generalized extended least squares algorithm.

关键词： Parameter estimation interactive Box-Jenkins models auxiliary model hierarchical identification

作者: Li Xie Huizhong Yang Feng Ding

作者单位: Control Science and Engineering Research Center, Jiangnan University,Wuxi, P.R.China 214122

会议类型: 国际会议

会议名称: International Conference on Life System Modeling and Simulation,and International Conference on Intelligent Computing for Sustainable Energy and Environment(2010生命系统建模与仿真国际会议暨m2010可持续能源与环境智能计算国际会议)

会议地点: 无锡

会议语种:英文

页码: 163-169

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）