Monotonic Normalized Radial Basis Function Networks

被引：0
|
作者：
Husek, Petr [1 ]
机构：
[1] Czech Tech Univ, Dept Control Engn, Fac Elect Engn, Tech 2, Prague 16627 6, Czech Republic
来源：
2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019) | 2019年
关键词：
Radial basis function networks; Prior knowledge; Monotonic regression; Least square approximation;
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：
In the paper we address the problem of deriving monotonicity conditions for normalized radial basis function networks. For general shape of the kernels the necessary conditions are expressed as trivial inequalities imposed on the kernel weights together with set of linear inequalities on elements of matrices describing the kernels. If the shape is considered to be the same for all kernels the conditions become simple and intuitive. Two examples are given to demonstrate benefit of incorporation of information about monotonicity.
引用
收藏
页码：3118 / 3123
页数：6
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