【作者】 杨樱；

【导师】 叶德谦；

【作者基本信息】 燕山大学 ， 计算机应用技术， 2006， 硕士

【摘要】 近年来,人工神经网络在建立非线性模型进行经济问题预测方面受到广泛重视和研究。本文将就这一课题进行进一步地研究。将BP神经网络应用在股票预测系统中,从而提高预测的非线性拟和能力是一个很好的新研究领域并取得了一定的进展。而这一方法还存在预测系统不太稳定,精确度和泛化能力不高的缺点,导致系统在后期预测中自学习能力不强的后果,使得在数据量预测时间加大的情况下预测结果难以有较强的参考价值。 为此,本文选择使用强化学习的方法来改进BP神经网络的股票预测方法,提高系统的稳定性和精确度,使系统的泛化能力和自学习能力得到了提高,并且在其中结合了神经网络集成的算法,达到了较好的效果。 (1)选取适当的股票数据进行预测的操作,保证数据具有一定的可参考价值。 (2)对股票数据进行适当的预处理,保证数据在神经网络的预测中能够具有较好的收敛性以及较快的收敛速度,使系统的性能得到一定的保障。 (3)采用恰当的神经网络集成算法,保障系统预测稳定,克服BP神经网络的预测的不稳定性,使得预测的结果具有较高的可参考性,预测的股票价格曲线具有较好的稳定性。 (4)对于时间序列的特性进行分析后,采用恰当的强化学习的算法,对神经网络的系统输出结果进行优化,并且确定较优的强化学习系统参数。 将以上算法思想在MATLAB环境中实现,利用VC++工具和MATLAB工具实现上述系统,并且确定理想的系统参数,验证设计的合理性。将系统设计的结果和设计系统的优越性进行系统的分析,验证设计的优越性。更多还原

【Abstract】 As establishing nonlinear mode using artificial neural network has been widely used to economy forecasting for recently years, this paper will do some research on the topic. Using BP neural networks on stock forecasting system is a new research field and has a great evolvement. But this method is not good at stability and definition. So it make the system’s self-study ability not strong, when the data quantum become bigger, the forecasting result become weaker reference value.In this paper, we provides the optimization on a reinforcement learning algorithm based on neural network. Using this method we can improve the system’s stability and definition and improve the generalization ability of learning system.(l)Choose proper socket data to operate, assure the data have a definite reference value.(2)Choose the proper method to pretreatment the data, assure the data have good astringency and faster constringency rate during the BP neural networks training process, the system performance gained definite ensure.(3)Adopt the appropriate algorithm based on neural network ensemble, assure the forecasting system’s stabilization, conquer the BP neural network system’s instability. So that the forecasting result have higher reference value and the forecasting stock price curve has better stability.(4)Analysis the characteristic of time serial, adopt the right reinforcement learning algorithm to optimize the result of the neural network’s output, choose the excellent parameter of the reinforcement learning system.All algorithms above are implemented in MATLAB, and utilizing VC++ and MATLAB realization the system, confirm excellent parameter through our test, validate the rationality of our system. Analysis the advantages of the designof our system, make sure that our design is succeed.更多还原