Advantages of Radial Basis Function Networks for Dynamic System Design

被引：342

作者：

Yu, Hao ^{[1
]}

Xie, Tiantian ^{[1
]}

Paszczynski, Stanislaw ^{[2
]}

Wilamowski, Bogdan M. ^{[1
]}

机构：

[1] Auburn Univ, Dept Elect & Comp Engn, Auburn, AL 36849 USA

[2] Univ Informat Technol & Management, Dept Distributed Syst, PL-35959 Rzeszow, Poland

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2011年 / 58卷 / 12期

关键词：

Adaptive control; fuzzy inference systems; neural networks; online learning; radial basis function (RBF) networks; BASIS NEURAL-NETWORKS; LEARNING ALGORITHM; FUZZY RULES; CONTROLLER; MOTOR;

D O I：

10.1109/TIE.2011.2164773

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Radial basis function (RBF) networks have advantages of easy design, good generalization, strong tolerance to input noise, and online learning ability. The properties of RBF networks make it very suitable to design flexible control systems. This paper presents a review on different approaches of designing and training RBF networks. The recently developed algorithm is introduced for designing compact RBF networks and performing efficient training process. At last, several problems are applied to test the main properties of RBF networks, including their generalization ability, tolerance to input noise, and online learning ability. RBF networks are also compared with traditional neural networks and fuzzy inference systems.

引用

页码：5438 / 5450

页数：13

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