基于改进BP神经网络的数字识别

针对BP(Back Propagation)神经网络易陷入局部极小、收敛速度慢的缺点,提出了一种新的BP神经网络改进算法.与标准BP算法比较,该系统通过结合附加动量法和自适应学习速率形成新的BP改进算法.附加动量法虽然可以使BP算法避免陷入局部极小,但是对初始值的选取比较敏感,而且选取合适的学习速率比较困难.而自适应学...

Numerical recognition based on improved BP neural network

In this paper,a new improved BP(Back Propagation) algorithm is presented,because BP neural network can easily fall into local minimum and slow convergence.Compared to the standard BP,this algorithm integrated the additional momentum method with the adaptive learning rate method.The BP algorithm could avoid falling into local minimum because of the additional momentum method,but this method was sensitive to the initial values,and it was also difficult to choose the appropriate learning rate.The adaptive learning rate method could adjust the learning rate to an appropriate value automatically and improve the convergence speed of the network,but it could not get rid of local minimum.By integrating these two methods,this new algorithm could get rid of local minimum and improve the convergence speed.According to this algorithm,a numerical recognition system based on BP neural network was designed.This system could be put into the application of numerical recognition such as bill system.Experiments demonstrate it that BP algorithm can successfully avoid falling into local minimum,and the convergence rate increases seventeen point five times than the standard BP algorithm.