Improvement of back propagation algorithm based on convolution neural network

In the real speech recognition system,in view of the problem of training efficiency caused by large-scale model parameters of convolutional neural network and mass training data,a narrowed weight range back propagation(NWBP)algorithm was proposed,which solved the oscillation phenomenon which was prone to error at the end of the network parameter training.The K-MEANS algorithm was used to obtain the seed node with the minimum error value.Through the iterative calculation process,the range of weight was reduced for avoiding the oscillation phenomenon,making the network error of training results converge,thereby,training efficiency was improved.Through the simulation experiment,the NWBP algorithm has an increase in the convergence effects comparing with the variable learning rate back propagation algorithm in the process of weight training of the convolution neural network,which reduces the redundancy calculation and shortens the training time to a certain extent.The effect of the algorithm is more significant than that in the simple network.