On Monotonic Radial Basis Function Networks

被引：2

作者：

Husek, Petr ^{[1
]}

机构：

[1] Czech Tech Univ, Fac Elect Engn, Dept Control Engn, Prague 16627, Czech Republic

来源：

IEEE TRANSACTIONS ON CYBERNETICS | 2024年 / 54卷 / 02期

关键词：

Radial basis function networks; Fuzzy systems; Support vector machines; Shape; Optimization; Task analysis; Standards; Least square approximation; monotonic regression; prior knowledge; radial basis function (RBF) networks; MAMDANI-ASSILIAN MODELS; NUMERICAL-SOLUTION; CLASSIFICATION; PREDICTION; REGRESSION; EQUATION;

D O I：

10.1109/TCYB.2022.3185827

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article deals with monotonicity conditions for radial basis function (RBF) networks. Two architectures of RBF networks are considered-1) unnormalized network with a local character of the basis function and 2) a normalized network where the value of RBF is taken relatively with respect to the others. Different approaches are followed for each of them. For the former, monotonicity is enforced in prescribed points whereas for the latter sufficient monotonicity conditions are formulated. In both cases, the monotonicity conditions are expressed as linear constraints on the network weights that enable efficient solving of the related optimization problems. Many illustrative examples are presented to show the advantages of incorporating prior information in the form of monotonicity.

引用

页码：717 / 727

页数：11

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