Training Radial Basis Neural Networks with the Extended Kalman Filter

Dan Simon
Department of Electrical Engineering
Cleveland State University
Cleveland, Ohio

Radial Basis Function (RBF) neural networks are three-layer neural networks. Several ways have been proposed for training RBF networks. Recently, Professor Simon has proposed the use of Kalman filters for training RBF networks [1]. This web page makes available the classical Iris data that can be used to test RBF networks, along with various m-files that can be run in the MATLAB environment. M-files are written in a very high-level language that can be easily read, almost like pseudo code. The data files and m-files are contained in the following zip file. - 16 kilobytes

If you download to your hard drive by clicking on the above link, then unzip the file (using, for example, WinZip), you can run RBF experiments and reproduce the results in reference [1]. These include results on gradient descent training, Kalman filter training, and decoupled Kalman filter training. When you unzip the file on your hard drive, look at the readme.txt file for more detailed information. If you don't have software to unzip the file, you can download a free evaluation version of WinZip from


  1. D. Simon, “Training Radial Basis Neural Networks with the Extended Kalman Filter,” Neurocomputing, vol. 48, pp. 455-475, October 2002 - pdf, 220 KB

Professor Simon's Home Page

Department of Electrical and Computer Engineering

Cleveland State University

Last Revised: December 13, 2013