Radial basis function networks, regression weights, and the expectation - Maximization algorithm

Reza Langari, Liang Wang, John Yen

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


We propose a modified radial basis function (RBF) network in which the regression weights are used to replace the constant weights in the output layer. It is shown that the modified RBF network can reduce the number of hidden units significantly. A computationally efficient algorithm, known as the expectation-maximization (EM) algorithm, is used to estimate the parameters of the regression weights. A salient feature of this algorithm is that it decomposes a complicated multiparameter optimization problem into L separate small-scale optimization problems, where L is the number of hidden units. The superior performance of the modified RBF network over the standard RBF network is illustrated by computer simulations.

Original languageEnglish (US)
Pages (from-to)613-623
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.
Issue number5
StatePublished - 1997

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Radial basis function networks, regression weights, and the expectation - Maximization algorithm'. Together they form a unique fingerprint.

Cite this