A new parallel back-propagation algorithm for neural networks
Peirong Ji, Peng Wang, Qin Zhao, Li Zhao · 2011
The BP neural network is one of the most widely used neural networks. It uses the back-propagation algorithm for training, and the algorithm has the disadvantage of slow convergence and long training time. In this paper, a parallel BP neural network algorithm with a balancing scheme of dynamic load is presented in order to reduce the training time of large scale neural networks. The experimental results indicate that the proposed algorithm has the feature of speeding-up computation for the large scale neural networks.