A global learning algorithm for a RBF network

Qiuming Zhu, Yao Cai, Luzheng Liu

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


This article presents a new learning algorithm for the construction and training of a RBF neural network. The algorithm is based on a global mechanism of parameter learning using a maximum likelihood classification approach. The resulting neurons in the RBF network partitions a multidimensional pattern space into a set of maximum-size hyper-ellipsoid subspaces in terms of the statistical distributions of the training samples. An important feature of the algorithm is that the learning process includes both the tasks of discovering a suitable network structure and of determining the connection weights. The entire network and its parameters are thought of evolved gradually in the learning process.

Original languageEnglish (US)
Pages (from-to)527-540
Number of pages14
JournalNeural Networks
Issue number3
StatePublished - Apr 1999


  • Competitive neuron layer
  • Hyper-ellipsoidal subspace
  • Maximum likelihood classification
  • RBF neural networks
  • Subclass clustering

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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