Published in Proceedings of the 2008 IEEE International Conference on Control Applications, September 1, 2008, pages 972-977.
The definitive version is available at https://doi.org/10.1109/CCA.2008.4629704.
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.
Electrical and Computer Engineering
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