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基于改進BP神經網絡的數(shù)字識別

1944742 2011-03-07 | rar | 784 KB | 次下載 | 10積分

資料介紹

?針對BP（Back Propagation）神經網絡易陷入局部極小、收斂速度慢的缺點，提出了一種新的BP神經網絡改進算法。與標準BP算法比較，該系統(tǒng)通過結合附加動量法和自適應學習速率形成新的BP改進算法。附加動量法雖然可以使BP算法避免陷入局部極小，但是對初始值的選取比較敏感，而且選取合適的學習速率比較困難。而自適應學習速率法可以自動把學習速率調整到一個合適的數(shù)值，也可以加快網絡的收斂速度，但不能避免陷入局部極小。通過將兩者結合起來形成新的改進算法，既可以避免陷入局部極小又可以加快網絡的收斂速度。并在此基礎上設計一個基于BP神經網絡的數(shù)字識別系統(tǒng)，此系統(tǒng)可以作為核心部分應用到諸如票據(jù)等數(shù)字識別中去。實驗結果表明，該方法成功的避免了BP算法陷入局部極小，而且收斂速度比標準BP算法提高了17.5倍。

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.