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

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