NDLI: A Recursive Growing and Pruning RBF (GAP-RBF) Algorithm for Function Approximations

Content Provider	IEEE Xplore Digital Library
Author	Guang-Bin Huang Saratchandran, P. Sundararajan, N.
Copyright Year	2003
Description	Author affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798. E-Mail: egbhuang@ntu.edu.sg (Guang-Bin Huang)
Abstract	A new sequential growing and pruning algorithm for RBF networks, referred to as GAP-RBF algorithm, has been proposed in our paper[1]. GAP-RBF algorithm modifies the growth criterion of Platt’s RAN and combines it with a new pruning strategy. Both the growing and pruning strategy is based on the link between the learning accuracy and the "significance" of the "nearest" or intentionally added new neuron. It has been shown that the GAP-RBF network outperforms RAN, RAN-EKF and MRAN in function approximation area. GAP-RBF significantly increases the learning speed and learning accuracy, and grows and prunes neurons much more smoothly. However, although GAP-RBF can reach higher learning accuracy than RAN, RANEKF, and MRAN, similar to RAN, RANEKF and MRAN, its learning error can be very high for certain applications. This paper proposed a recursive learning based sequential learning algorithm for Growing and Pruning RBF (GAP-RBF) networks. This new algorithm is called Recursive-GAP-RBF and thus, the original GAP-RBF can be called Basic-GAP-RBF. If no new observation comes to the network, Recursive-GAP-RBF can recursively self adjust its Parameters based on its historic observations. Whenever a new observation has been input to the network, Recursive-GAP-RBF works like a basic GAP-RBF.
Sponsorship	Lee Found. IEEE Montreal Sect. Candian Soc. Mechanical Eng
Starting Page	491
Ending Page	495
File Size	347240
Page Count	5
File Format	PDF
ISBN	078037777X
DOI	10.1109/ICCA.2003.1595070
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-06-12
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Function approximation Radio access networks Neurons Radial basis function networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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