Multi-model recursive identification for nonlinear systems with non-uniformly sampling

被引：10
作者：
Liu, Ranran [1 ,2 ]
Pan, Tianhong [2 ]
Li, Zhengming [2 ]
机构：
[1] Jiangsu Univ Technol, Sch Automot & Traff Engn, Changzhou 213001, Peoples R China
[2] Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Jiangsu, Peoples R China
来源：
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2017年 / 20卷 / 01期
关键词：
Non-uniformly sampled-data; Nonlinear systems identification; Fuzzy c-mean cluster; Multi-model method; Recursive least squares; PARAMETER-IDENTIFICATION; ITERATIVE IDENTIFICATION; MODEL; ALGORITHM;
D O I：
10.1007/s10586-016-0688-0
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
recursive least squares based on Multi-model is proposed for non-uniformly sampled-data nonlinear (NUSDN) systems. The corresponding state space model of an NUSDN system is derived using lifting technique. Taking advantage of the Fuzzy c-Mean Clustering algorithm, NUSDN is divided into several local models. The basic idea is that the NUSDN system is viewed as a model switching system under a given rule. Once the local models are identified, the global model is determined. A pH neutralization process validate the performance of the proposed algorithm.
引用
收藏
页码：25 / 32
页数：8
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