Published in Proceedings of the 1998 IEEE International Conference on Control Applications, Volume 1, September 1, 1998, pages 293-297.
NOTE: At the time of publication, the author Xiao-Hua Yu was not yet affiliated with Cal Poly.
The definitive version is available at http://dx.doi.org/10.1109/CCA.1998.728427.
The choice of network dimension is a fundamental issue in the design of artificial neural networks. A larger neural network is powerful for solving problems while a smaller neural network is always advantageous in real-time environment where speed is crucial. In this paper, a network pruning algorithm with embedded gradient-conjugate training is investigated and applied to the identification of a large flexible space structure. Computer simulation results show that this approach can dramatically reduce the size of neural network while maintaining compatible identification accuracy.
Electrical and Computer Engineering
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