I/O data-based multiple-model modeling method

被引：0
|
作者：
Han, Zhaochao [1 ]
Huang, Shucai [1 ]
Shao, Lei [1 ]
机构：
[1] Missile Institute, Air Force Engineering University, Sanyuan 713800, China
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2010年 / 38卷 / 06期
关键词：
Clustering algorithms - Parameter estimation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A multiple-model modeling method based on the I/O data is suggested for the complex nonlinear system. Firstly, fuzzy partition was employed to on-line clustering of the I/O data. Then, the least-squares (LS) algorithm was used to construct the local model for each clustering, and the parameters of each local model were updated according to the new data. The proposed algorithm takes advantage of space decomposition of the fuzzy model, combines the fuzzy partition and multiple-model modeling, and updates on-line the number of the local model and the parameter of each, so as to realize the on-line modeling for the complex nonlinear system. Simulation result shows the effectiveness of this method.
引用
收藏
页码：28 / 31
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