Explicit Center Selection and Training for Fault Tolerant RBF Networks

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 12 - Chapter in an edited book (Author)peer-review

1 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationNeural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
EditorsTom Gedeon, Kok Wai Wong, Minho Lee
PublisherSpringer
Pages273-285
ISBN (Electronic)9783030367114
ISBN (Print)9783030367107
Publication statusPublished - 2019

Publication series

NameLecture Notes in Computer Science
Volume11954
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title26th International Conference on Neural Information Processing (ICONIP 2019)
PlaceAustralia
CitySydney
Period12 - 15 December 2019

Abstract

Although some noise tolerant center selection training algorithms for RBF networks have been developed, they usually have some disadvantages. For example, some of them cannot select the RBF centers and train the network simultaneously. Others do not allow us to explicitly define the number of RBF nodes in the resultant network, and we need to go through a time consuming procedure to tune the regularization parameter such that the number of RBF nodes used satisfies our pre-specified value. Therefore, it is important to develop some noise resistant algorithms that allow us to specify the number of RBF nodes in the resultant network. In addition, they should be able to train the network and to select RBF nodes simultaneously. This paper formulates the RBF training problem as a generalized M-sparse problem. We first define a noise tolerant objective function for RBF networks. Afterwards, we formulate the training problem as a generalized M-sparse problem, in which the objective function is the proposed noise tolerant training objective function and the constraint is an l0-norm of the weight vector. An iterative algorithm is then developed to solve this generalized M-sparse problem. From simulation experiments, the proposed algorithm is superior to the state-of-art noise tolerant algorithms. In addition, the proposed algorithm allows us to explicitly define the number of RBF nodes in the resultant network. We prove that the algorithm converges and that the fixed points of the proposed algorithms are the local minimum of this generalized M-sparse problem.

Research Area(s)

  • Center selection, Fault tolerance, RBF network

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Explicit Center Selection and Training for Fault Tolerant RBF Networks. / Wong, Hiu Tung; Wang, Zhenni; Leung, Chi-Sing et al.
Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings. ed. / Tom Gedeon; Kok Wai Wong; Minho Lee. Springer, 2019. p. 273-285 (Lecture Notes in Computer Science; Vol. 11954).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 12 - Chapter in an edited book (Author)peer-review