Recommended Citation
Published in Proceedings of the 2008 IEEE International Conference on Control Applications, September 1, 2008, pages 972-977. Copyright © 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. The definitive version is available online at: http://dx.doi.org/10.1109/CCA.2008.4629704.
Abstract
Choosing an appropriate size of a network is an important issue for any neural network applications. The common practice is to start with an “over-sized” network, then gradually reduces its size to find the optimal solution. In this paper, a new hybrid neural network pruning algorithm for multi-layer feedforward neural networks is investigated. Computer simulation results on system identification and pattern classification problems show this algorithm can significantly reduce the network dimension while still maintaining satisfactory identification and classification accuracy.
Disciplines
Electrical and Computer Engineering
URL: http://digitalcommons.calpoly.edu/eeng_fac/109