DocumentCode
441935
Title
Learning the architecture and parameters of RBF network based on hybrid IPL algorithm
Author
Zhu, Gen-Biao ; Zhang, Feng-Ming ; Wang, Jin-Gan ; Shi, Jun-Yong
Author_Institution
Coll. of Eng., Air Force Eng. Univ., Xi´´an, China
Volume
5
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
2919
Abstract
A new method of constructing RBF network based on hybrid incremental projection-learning algorithm (HIPLA) is presented. The method substitutes aspiration criteria for original error to simplify network architecture and improve its approximation. It only needs a small number of sampling data, and its training speed is higher than the traditional one. The computer simulation results show that the output of the system is accurate.
Keywords
learning (artificial intelligence); radial basis function networks; RBF network architecture optimization; hybrid incremental projection-learning algorithm; radial basis function network; Approximation algorithms; Computer architecture; Computer errors; Computer simulation; Equations; Function approximation; Kernel; Radial basis function networks; Sampling methods; Vectors; Aspiration criteria; Background theory; Hybrid IPL algorithm (HIPLA); RBF Network Architecture optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527441
Filename
1527441
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