Memory efficient BFGS neural-network learning algorithms using MLP-network: a survey

@article{Asirvadam2004MemoryEB,
  title={Memory efficient BFGS neural-network learning algorithms using MLP-network: a survey},
  author={V. S. Asirvadam and S. F. McLoone and G. W. Irwin},
  journal={Proceedings of the 2004 IEEE International Conference on Control Applications, 2004.},
  year={2004},
  volume={1},
  pages={586-591 Vol.1}
}
This paper surveys various implementation of a memory efficient second order (Broyden, Fletcher, Goldfard and Shanno) BFGS training algorithms which includes novel optimal memory (OM) BFGS neural network training algorithm, proposed by the present authors, which optimises performance in relation to available memory. Simulation results using a control benchmark problems show that OM BFGS, which is mathematically equivalent to full memory (FM) BFGS training when there are no constraints on memory… CONTINUE READING