共查询到20条相似文献,搜索用时 453 毫秒
1.
文章尝试将两种独立的灰色模型预测方法,GM(1,1)模型与Verhulst模型结合起来考虑,形成一种新的灰色组合预测模型方法.为灰色模型应用于近期、中长期的预测提供了一个新的解决方案。 相似文献
2.
3.
4.
利用改进的GM(1,1,λ)模型预测舰艇批量生产成本 总被引:2,自引:0,他引:2
在分析GM(1,1,λ)模型的建模机理的基础上,指出传统建模方法存在的不足,基于GM(1,1,λ)模型的还原数据模型与原始序列的第一点无关,提出一种可以完全利用全部已知信息的建模方法。同时给出基于遗传算法的GM(1,1,λ)优化模型,优化模型提高了灰色预测的精度。探讨了应用灰色理论建立舰艇批量生产成本预测模型的可行性,并给出了应用实例。 相似文献
5.
基于灰色系统理论的集装箱海铁联运量预测 总被引:1,自引:0,他引:1
介绍灰色系统理论,提出基于灰色系统理论的多变量灰色系统数列预测模型——PGM(1,N)模型,推导出用PGM(1,N)模型进行灰色数列预测的基本公式。以大连港为例,预测未来5a大连港集装箱吞吐量、集装箱海铁联运运量,分析表明此方法对铁路集装箱海铁联运量预测的有效性和实用性,且适合编程。 相似文献
6.
7.
为了降低港口集装箱吞吐量的预测误差,提高预测精度,文章通过分析传统的灰色预测模型和 BP 神经网络预测模型的优缺点,构建了灰色神经网络港口集装箱吞吐量预测模型,该模型充分发挥了灰色模型所需初始数据少和 BP 神经网络非线性拟合能力强的特点。以实际数值作为初始数据,各种灰色模型的预测值为神经网络的输入值,神经网络的输出值为组合预测结果。通过实例分析,结果表明:灰色神经网络预测模型提高了预测精度,预测结果比较理想,优于单一预测模型,因此,该模型用于港口集装箱吞吐量预测是可行的、有效的。 相似文献
8.
针对港口货物吞吐量预测的影响因素不确定及统计数据缺乏的问题,引入灰色预测理论,在传统的线性GM(1,1)模型的基础上。运用非线性GM(1,1,α)模型对港口货物吞吐量数据进行模拟和预测,并以2003-2007年汕头港货物吞吐量数据为实证,检测该模型的实用性和准确性。 相似文献
9.
10.
11.
Ship motion, with six degrees of freedom, is a complex stochastic process. Sea wind and waves are the primary influencing factors. Prediction of ship motion is significant for ship navigation. To eliminate errors, a path prediction model incorporating ship pitching was developed using the Gray topological method, after analyzing ship pitching motions. With the help of simple introduction to Gray system theory, we selected a group of threshold values. Based on an analysis of ship pitch angle sequences over 40 second intervals, a Grey metabolism GM(1,1) model was established according to the time-series which every threshold corresponded to. Forecasting future ship motion with the GM (1,1) model allowed drawing of the forecast curve with effective forecasting points. The precision of the test results show that the model is accurate, and the forecast results are reliable. 相似文献
12.
水下机器人传感器故障诊断的灰色预测模型 总被引:1,自引:0,他引:1
将灰色预测GM(1,1)原理引入到水下机器人传感器的故障诊断中,对传感器样本数据序列建立灰色动态预测型。通过对该模型输出信号与实际输出之间误差的分析,实时检测传感器的故障。针对Outland 1000无人水下机器人中的方向传感器,应用该方法对该传感器的三种典型故障模式进行了故障检测实验,结果表明所提故障检测方法准确可靠。 相似文献
13.
14.
灰色预测的拓扑选择在运量预测中的运用 总被引:1,自引:0,他引:1
熊如 《交通部上海船舶运输科学研究所学报》1993,(2)
运用灰色系统理论及GM模型拓朴选择,建立了相应的GM(1,1)模型,运用于运量预测,并以港口吞吐量与货物周转量的预测为例进行校验。结果表明,新息GM(1,1)模型,新陈代谢GM(1,1)模型与原始模型(全数据GM(1,1)模型)三者比较,新陈代谢GM(1,1)模型的精度最高,值得推荐在运量预测中采用。 相似文献
15.
16.
17.
港口吞吐量的预测是港口规划过程中最为基础也最为关键的一步,只有对港口吞吐量做出准确、稳定的预测,才能做出科学合理的港口发展规划。由于内河港吞吐量历史数据有限,文中采用GM(1,1)和Verhulst模型的最优组合模型对港口吞吐量进行预测。该组合模型充分利用GM(1,1)模型“少数据,短期预测准确”的优点,又针对GM(1,1)预测量的无限增大趋势,引入了Verhulst模型进行组合修正,进而提高预测值的准确、稳健性。 相似文献
18.
灰色系统模型在内河港口吞吐量预测中的应用 总被引:2,自引:1,他引:1
根据淮南港吞吐量实际调查资料,选择灰色系统理论对其进行吞吐预测研究,结果表明,对不同的预测时期应采用不同的灰色系统预测模型。对于短期预测,采用GM(1,1)模型与Verhulst模型的组合模型;对于长期预测,采用Verhulst模型并用GM(1,1)模型对其残差进行修正。实例验证以上两种模型是可行性的。 相似文献
19.
[Objective]The propulsion shafting system is an important part of a ship, and the bearing load directly affects its operating state and service life. In this paper, bearing load under hull deformation is studied using grey system theory. [Method] First, according to the empirical formula of the relative displacement of each bearing caused by the deformation of the hull of a 57 000 DWT oil tanker, the relative displacement of each bearing is calculated and input into a finite element model, and the load value of each bearing is output. On this basis, grey relationship analysis of grey system theory is introduced to study the influence degree of stern bearing displacement on the load of each bearing, and the relative change of the load of each bearing caused by the displacement of the stern bearings is analyzed. A GM (1,1) prediction model is then established for the bearing load considering the bearing displacement conditions, and the hull deformation-fitting and prediction of each bearing load are made. [Results]The results show that grey relationship analysis can effectively reflect the influence of hull deformation on bearing load. The GM (1,1) prediction model has high accuracy and prediction errors less than 6.0%, and the model test indexes can represent the accuracy of the prediction. [Conclusion]Grey system theory is effective and practical in research on propulsion shaft load. It can accurately predict bearing load under bearing displacement, giving it certain reference value for research on bearing load under actual sailing conditions. © The Author(s) 2022. 相似文献
20.
SHEN Ji-hong ZHAO Xi-ren College of Science Harbin Engineering University Harbin China College of Automation Harbin Engineering University Harbin China 《船舶与海洋工程学报》2002,1(1):65-68
GM(1, 1) is generally used in Grey System Theory which constructs an Ordinary Differential Equation for given se-ries. It is effective for monotone series, and its simulating effect is good and error is small. However, If the series dosen' t havea property of monotone, the simulating effect of GM(1,1) is not fine, and its error gets bigger. In this paper, we use GM(2,1) to handle the oxcillation series, which uses the Method of Minimum Squares in determining the uncertain parameters.The 相似文献