A parsimonious radial basis function-based neural network for data classification


Tan, Shing Chiang and Lim, Chee Peng and Watada, Junzo (2015) A parsimonious radial basis function-based neural network for data classification. In: Intelligent Decision Technology Support in Practice. Smart Innovation, Systems and Technologies (SIST,, 42 (42). Springer International Publishing, pp. 49-60. ISBN 978-3-319-21209-8

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The radial basis function neural network trained with a dynamic decay adjustment (known as RBFNDDA) algorithm exhibits a greedy insertion behavior as a result of recruiting many hidden nodes for encoding information during its training process. In this chapter, a new variant RBFNDDA is proposed to rectify such deficiency. Specifically, the hidden nodes of RBFNDDA are re-organized through the supervised Fuzzy ARTMAP (FAM) classifier, and the parameters of these nodes are adapted using the Harmonic Means (HM) algorithm. The performance of the proposed model is evaluated empirically using three benchmark data sets. The results indicate that the proposed model is able to produce a compact network structure and, at the same time, to provide high classification performances.

Item Type: Book Section
Uncontrolled Keywords: Radial basis function neural network, Adaptive resonance theory, Harmonic mean algorithm, Classification
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 17 Jul 2020 07:52
Last Modified: 17 Jul 2020 07:52
URII: http://shdl.mmu.edu.my/id/eprint/6801


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