A neural network pruning algorithm with embedded gradient-conjugate training for the identification of large flexible space structures

Xiao-Hua Yu
Proceedings of the 1998 IEEE International Conference on Control Applications (Cat. No.98CH36104)  
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 realtime 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
more » ... ch can dramatically reduce the size of neural network while maintaining compatible identification accuracy. Abstract 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.
doi:10.1109/cca.1998.728427 fatcat:4kgrtxs3m5hg7akvtyvy2fzbya