The RBF neural network in approximate dynamic programming

被引：0
|
作者：
Ster, B [1 ]
Dobnikar, A [1 ]
机构：
[1] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana 61000, Slovenia
来源：
ARTIFICIAL NEURAL NETS AND GENETIC ALGORITHMS | 1999年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A radial basis function (RBF) neural network was applied to an optimal control problem. The role of an approximation architecture in the task of dynamic programming is emphasised. While it has been proved that dynamic programming works well for moderate discrete spaces, research is continuing on how to apply dynamic programming techniques to large discrete and continuous spaces. For continuous spaces there does not yet exist a universal approach, but it seems that a RBF network is able to solve the problem with a negligible amount of manual experimentation.
引用
收藏
页码：161 / 165
页数：5
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