灰色系统与神经网络算法相结合的分析与研究

摘要

When the BP neural network is used to predict the model parameters, the learning training speed and modeling time of algorithm are longer.when the gray system’s theory is used to forecast model parameters, the study and training ability of data information are limited. These two algorithms have their own defects. The grey system’s theory is combined with BP neural network algorithm in this paper to improve the convergence speed and estimation precision in the model. It can be seen from the simulation that the valuation accuracy of model parameter is higher, smaller error, proving the effectiveness and robustness of the algorithm.%采用 BP 神经网络对模型参数进行预测，算法的学习训练速度和建模时间比较长；采用灰色系统理论对模型参数进行预测，对数据信息的学习和训练能力比较有限，两种算法都存在各自的缺陷，为了提高模型中参数的收敛速度和估计精度，本文将灰色系统理论和BP神经网络算法相融合，通过仿真可以看出，模型参数的估值精度比较高，误差较小，证明了该算法的有效性和鲁棒性。