## Abstract

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 language | English (US) |
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Pages (from-to) | 613-623 |

Number of pages | 11 |

Journal | IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans. |

Volume | 27 |

Issue number | 5 |

DOIs | |

State | Published - 1997 |

## All Science Journal Classification (ASJC) codes

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