Pareto RBF networks based on multiobjective evolutionary computation

被引：0
|
作者：
Kondo, N [1 ]
Hatanaka, T [1 ]
Uosaki, K [1 ]
机构：
[1] Osaka Univ, Dept Informat & Phys Sci, Grad Sch Informat & Phys Sci, Suita, Osaka 5650871, Japan
来源：
SICE 2004 ANNUAL CONFERENCE, VOLS 1-3 | 2004年
关键词：
RBF network; evolutionary multi-objective optimization; genetic algorithm; nonlinear system modeling;
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：
In this paper, multi-objective structure selection of RBF networks based on evolutionary computation is considered. The RBF network structures are encoded to the chromosomes in GA. Then, they evolve toward Pareto optimum defined by some objective functions concerning with model accuracy, model complexity and connection weights. Some numerical simulation results indicate that the proposed approach can give Pareto optimum structures.
引用
收藏
页码：2177 / 2182
页数：6
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