Title :
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
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;
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
DOI :
10.1109/CCDC.2015.7162430
