A new recurrent radial basis function network

Hu Ming Cheung

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

4 Citations (Scopus)


Cheung and Xu (2001) has presented a dual structural recurrent radial basis function (RBF) network by considering the different scales in net's inputs and outputs. However, such a network implies that the underlying functional relationship between the net's inputs and outputs is linear separable, which may not be true from a practical viewpoint. In this paper, we therefore propose a new recurrent RBF network. It takes the net's input and the past outputs as an augmented input in analogy with the one in Billings and Fung (1995), but introduces a scale tuner into the net's hidden layer to balance the different scales between inputs and outputs. This network adaptively learns the parameters in the hidden layer together with those in the output layer. We implement this network by using a variant of extended normalized RBF (Cheung and Xu (2001)) with its hidden units learned by the rival penalization controlled competitive learning algorithm. The experiments have shown the outstanding performance of the proposed network in recursive function estimation.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publicationComputational Intelligence for the E-Age
EditorsLipo Wang, Jagath C. Rajapakse, Kunihiko Fukushima, Soo Young Lee, Xin Yao
Number of pages5
ISBN (Electronic)9789810475246
ISBN (Print)9810475241
Publication statusPublished - Nov 2002
Externally publishedYes
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
Duration: 18 Nov 200222 Nov 2002


Conference9th International Conference on Neural Information Processing, ICONIP 2002

Scopus Subject Areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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