Multi-Step-Ahead Optimal Learning Strategy for Local Model Networks with Higher Degree Polynomials

被引：0
|
作者：
Baenfer, Oliver [1 ]
Kampmann, Geritt [1 ]
Nelles, Oliver [1 ]
机构：
[1] Univ Siegen, Dept Mech Engn, D-57068 Siegen, Germany
来源：
2011 AMERICAN CONTROL CONFERENCE | 2011年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The idea of a learning strategy extension for nonlinear system identification with local polynomial model networks is presented in this paper. Usually the polynomial model tree (POLYMOT) algorithm utilizes a one-step-ahead optimal learning strategy. A demonstration example shows that this greedy behavior is not the best choice to reach a satisfying global model. Thus this strategy should be enlarged to a multi-step-ahead optimal learning. Therefore, it is possible to find the optimal global model in a special case.
引用
收藏
页码：2448 / 2449
页数：2
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