Structure and algorithm of interval RBF neural network

Author

Guan Shou-ping ; Li Han-lei ; Ma Ya-hui ; You Fu-qiang

Author_Institution

Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China

fYear

2015

fDate

23-25 May 2015

Firstpage

2951

Lastpage

2954

Abstract

This paper presents a structure and learning algorithm for interval Radial Basis Function (RBF) neural network. The subtractive clustering algorithm combined with the BP algorithm is used to train the neural network, which can cluster properly based on the set of interval data, and leading to obtain both the parameters of radial basis function and the number of clustering center, and to improve the mapping capability of neural network. The simulation results show that the converging and the approximating ability of the interval RBF are both better then the interval BP neural network.

Keywords

backpropagation; pattern clustering; radial basis function networks; BP algorithm; clustering center; interval BP neural network; interval RBF neural network; interval data; interval radial basis function neural network; learning algorithm; mapping capability; neural network training; subtractive clustering algorithm; Approximation algorithms; Approximation methods; Biological neural networks; Clustering algorithms; Data models; Neurons; BP algorithm; Interval RBF neural network; Subtractive clustering algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2015 27th Chinese

Conference_Location

Qingdao

Print_ISBN

978-1-4799-7016-2

Type

conf

DOI

10.1109/CCDC.2015.7162430

Filename

7162430