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中文摘要：
BP算法是应用广泛的神经网络算法，具有较强的非线性拟合能力，可以用来预测非线性时间序列数据的发展趋势。但在实际应用和仿真过程中，由于算法本身的限制和不足，对于仿真和计算都会带来很多问题，比如网络训练过程中程序异常中止、训练时间过长、仿真精度不高等。针对这样的情况。通过分析算法本身和训练仿真过程，找到了相应的原因和解决方法，研究了传统BP神经网络模型的缺陷并提出附加动量的方法来改进BP神经网络，最后通过在Matlab仿真环境下的实际仿真过程，验证了改善效果。
英文摘要：
BP（back propagation） algorithm which is one of the most widely used neural network algorithms, has very high nonlinear fitting ability, and it can be used to predict the developing trend of time series data in practical application and simulation. But some kinds of problems and exceptions may happen because of the limitation and deficiency of the algorithm itself, such as abnormal termination, long training time and low accuracy. Aiming at improving the petrol, through analyzing the algorithm and simulation, corresponding causes and problem - solving way are found. In this paper scene limitations of BP neural network have been analyzed and optimized methods have been supposed. Finally, through practical simulating experiments in Matlab, the effect is certificated.
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