NDLI: Using self-organizing incremental neural network (SOINN) For radial basis function networks

Content Provider	IEEE Xplore Digital Library
Author	Jie Lu Furao Shen Jinxi Zhao
Copyright Year	2014
Description	Author affiliation: State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China (Jie Lu; Furao Shen; Jinxi Zhao)
Abstract	This paper presents a batch learning algorithm and an online learning algorithm for radial basis function networks based on the self-organizing incremental neural network (SOINN), together referred to as SOINN-RBF. The batch SOINN-RBF is a combination of SOINN and least square algorithm. It achieves a comparable performance with SVM for regression. The online SOINN-RBF is based on the self-adaption procedure of SOINN and adopts the growing and pruning strategy of the minimal resource allocation network (MRAN). The growing and pruning criteria use the redefined significance, which is originally introduced by the growing and pruning algorithm for RBF (GGAP-RBF). Simulation results for both artificial and real-world data sets show that, comparing with other online algorithms, the online SOINN-RBF has comparable approximation accuracy, network compactness and better learning efficiency.
Starting Page	2142
Ending Page	2148
File Size	744198
Page Count	7
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889649
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radial basis function networks Radio access networks Moon Support vector machines Approximation algorithms Accuracy Least squares approximations
Content Type	Text
Resource Type	Article

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