基于改進BP神經(jīng)網(wǎng)絡(luò)的數(shù)字識別

Abstract:

?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.

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