基于灰色新陈代谢马尔可夫模型的飞行事故预测

灰色预测适用于时间短、数据量少和波动不大的预测问题,在长期预测时,数据序列拟合较差,预测精度偏低;而马尔可夫链适用于长期、数据序列随机波动大的预测问题。笔者结合灰色新陈代谢GM(1,1)模型和马尔可夫链理论的优点,建立飞行事故预测模型。模型去掉已失去参考价值的历史老信息,补充新信息,克服了随机波动性数据对飞行事故预测精度的影响,提高了灰色预测的应用水平。实例预测1973—2008世界飞行事故,其结果证明了灰色新陈代谢马尔可夫GM(1,1)模型预测精度较高,可用于飞行事故预测,具有较强的科学性和实用性。

Civil Aviation Accident Forecasting Based on Gray Metabolism Markov Model

Gray theory prediction is suitable for this kind of system with the characteristics of few data, short-time running and little fluctuation, and not suitable and precise for long term prediction. However, Markov chain theory is just the reverse. By combining the advantages of both Gray prediction and Markov prediction chain theory, a new Gray Markov metabolism GM(1,1) model is proposed. The new model gets rid of old information and accepts new information, and therefore avoids the effect of random fluctuation data on the prediction precision of aviation accidents and improves the application of scope of grey forecast. The forecasting result of worldwide civil aviation accidents from 1973 to 2008 by this method shows that this method is practical and scientific.