Time varying, nonlinear Ar model identification: Lainiotis' multi model methodology

作     者：Demiris, EN Likothanassis, SD 

作者机构：Univ Patras Dept Comp Engn & Informat GR-26000 Patras Greece Univ Patras Artificial Intelligence Res Ctr GR-26000 Patras Greece CTI Patras 26100 Greece 

出 版 物：《STOCHASTIC ANALYSIS AND APPLICATIONS》 (随机分析与应用)

年 卷 期：2002年第20卷第5期

页      面：911-926页

核心收录：

学科分类：07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Nonlinear AR model Multi-model partitioning theory System identification 

摘      要：This paper addresses the Nonlinear AutoRegressive (NAR) identification problem in connection with the choice of the time varying model structure and computation of the system coefficients. We introduce an-intelligent method,that is based on the reformulation of the problem in the standard state space form and the subsequent implementation of a bank of Extended Kalman filters, each fitting a different nonlinear model. The problem is reduced then to selecting the true model, using the well known Lainiotis Multi-Model Partitioning (MMP) theory, for general (not necessarily Gaussian) data pdf s. Simulations illustrate that the proposed method selects the correct nonlinear model, tracks successfully changes in the model structure and identifies the model parameters, in a sufficiently small number of iterations, in real time.