考虑复杂搭接关系的灰色网络计划模型

将复杂搭接关系引入灰色网络计划模型,并对搭接关系组合进行了分析.其中,活动持续时间与搭接时距都为灰数且服从三角白化权函数分布并使用核和灰度形式表示,在时间参数计算中引用基于论域的灰度计算法则以解决核与灰度加减运算结果与原灰区间不符的问题,并改进基于相对核与精确度的灰数排序方法以适应本模型考虑白化权函数的情况,同时将关键...     

苎麻纤维细度测试与分析的灰色模型

基于灰色系统理论的灰色建模应用于试验数据的相关分析 ,比基于数理统计的回归分析在实际应用中由于所需样本容量较小而具有明显的优点 ,特别对贫信息系统适用 .从而可避免在回归分析中因样本容量太小而致使回归方程的误差不可预测的弊端 .鉴于上述原理 ,本文采用灰色系统理论建立了苎麻纤维Tex数 Y与投影宽度 X之间的灰色 GM( 1 ,2 )模型 ,并进行了误差分析 .利用此模型探索了通过测定苎麻纤维投影宽度来计算其 Tex数的方法 .为生产工艺控制、产品质量检验和监督提供了一种简便而科学的办法 .     

基于三角模糊数序列的灰色预测模型

以GM(1,1)模型为代表的灰色预测模型是以精确数序列为基础,难以满足实际需要.为了使灰色模型适应于模糊数序列,具体给出了一种基于三角模糊数序列的建模方法,这种方法也可以实现对二元区间模糊数和梯形模糊数序列的建模.首先由三角模糊数序列得出三个含有等量信息的精确数序列:重心序列、隶属函数的覆盖面积序列和中界点序列,对这三个序列分别建模后,再导出原始三角模糊数序列的三个界点的预测模型.这种建模方法既保持了模糊数的整体性又提高了建模序列的光滑度,提高了预测精度.最后进行了多组随机三角模糊数序列的数据模拟,验证了模型的有效性.     

基于核和测度的区间灰数预测模型

区间灰数的确定与其核和测度有关,通过计算灰数的核和测度,将区间灰数转变成实数.分别对区间灰数核序列和测度序列建立预测模型,再推导还原得到区间灰数的预测模型.并将此模型应用于黄河内蒙段巴彦高勒站冰期日均流量预测,结果验证了所建模型的有效性和实用性.     

基于白化权函数的区间灰数预测模型

针对传统灰色预测模型无法进行白化权函数已知的区间灰数预测的缺陷,通过先将区间灰数进行标准化处理,分解成实数形式的"白部"和"灰部",然后分别对"白部"和"灰部"序列进行预测.再将已知的白化权函数映射为[0,1]区间上的函数,并用函数的面积和重心估计出预测值的白化权函数.模型不仅能解决典型白化权函数的类型,还能解决三角白化权函数的情况,且建模机理简单,计算简便.最后,将模型应用于黄河宁蒙河段巴彦高勒站的凌期日均流量的预测,验证了新模型的有效性及实用性.     

A new multivariable grey prediction model with structure compatibility

A new multivariable grey prediction model was proposed by adding a dependent variable lag term, a linear correction term and a random disturbance term to the traditional GM(1,N) model. It was theoretically proved that the new model can be completely compatible with the mainstream single variable and multivariable grey prediction models by adjusting and changing the model's parameters. To test the performance of the new model, three case studies were performed. The simulation and prediction results of the new model were compared with those of other grey prediction models. Results showed that the new model had evidently superior performance to other grey models, which confirms that the structure design of the new model is more reasonable than those of the other existing grey prediction models.     

基于区间灰色区间直觉不确定语言集的工程项目风险辨识和评价

针对具有信息灰性、模糊性及语言描述性的工程项目风险问题,定义区间灰色区间直觉不确定语言集用于表达这些特征,结合多属性群决策理论和C-OWA算子,构建工程项目风险辨识和风险评价模型,以南京市纬三路过江隧道为例验证所建模型的可行性。结果表明,该模型可以利用区间灰度、区间隶属度和区间非隶属度以及不确定语言变量,更全面、更真实地表达工程项目的实际信息,得到更符合工程实际的风险辨识和评价结果,帮助项目管理者更准确地预知主要风险因素和风险状态。     

Multi-variable grey model based on dynamic background algorithm for forecasting the interval sequence

The multi-variable grey model based on dynamic background algorithm improves the forecasting performance of the multi-variable grey model on the precise number sequence. In order to make this model suitable for the interval sequence, the matrix form of the multi-variable grey model based on dynamic background algorithm is proposed in the paper. In the modeling process, the interval is treated as a two-dimensional column vector, the parameters of the multi-variable grey model are replaced by matrices, and the dynamic background algorithm for interval sequences is proposed. The analysis results of the matrix algorithm for the dynamic background value and the prediction formula show that the new model is essentially a way to predict one of the two bounds of an interval by combining them, reflecting the integrity and interaction between the lower and upper bounds. The interval predictions of industrial electricity consumption of Zhejiang Province, China national electricity consumption and consumer price index show that the new model can well predict the minimum and maximum values of the interval sequence and has better prediction performance compared with the method of predicting each boundary sequence separately.     

GPM(1,1,m)模型及其在中国能源消费预测中的应用

客观准确地预测能源消费,可以为政府制定社会经济发展政策提供重要参考.利用矩阵分析的思想研究了灰色预测模型的建模机理,提出了基于时间多项式的可拓形式GPM(1,1,m)模型,并分析了其理论意义.在此基础上,通过研究了时间多项式对模型参数和预测值的影响,推导了它们之间的定量关系,设计了实际建模中的优化方法和参数估计的一般形...     

基于MFGM企业项目链风险元传递研究

为了对企业项目间风险元传递预测进行研究,本文首先提出项目链风险元传递概念及企业项目链风险传递四面体模型,然后以企业项目链风险传递链式结构为切入点,引入灰色预测、傅里叶级数和马尔科夫链理论,构建了马尔科夫—傅里叶级数修正灰色预测模型(MFGM),用来预测项目链式结构的风险元传递,最后以项目工期为风险元,算例分析基于该模型的项目链风险元传递的预测情况,结果表明该模型具有可行性和有效性。     

改进不等时距灰色GM(1,1)模型及其应用

针对传统不等时距灰色模型(UGM(1,1))的缺陷,分别从背景值优化和模型优化,结合新陈代谢思想,提出了2种改进不等时距灰色模型:新陈代谢不等时距加权灰色模型(MUGM(1,1,w))和改进新陈代谢不等时距灰色模型(AMUGM(1,1)).经算例仿真检验,结果表明:2种改进模型在预测精度和实用性上都优于传统UGM(1,1)模型,其中AMUGM(1,1)模型更是克服了灰色模型不含线性因素的局限,拓宽了灰色模型的适用范围,具有很好的应用价值.     

基于标准区间灰数的发展带离散DDGM预测模型

由于区间灰数运算体系尚不完善,灰数间的代数运算将导致结果灰度增加,难以有效构建基于"区间灰数"的灰色发展带预测模型.对此,通过将区间灰数进行标准化处理,分解成基于实数形式的"白部"和"灰部"两个部分;然后分别对"白部"和"灰部"建立发展带预测模型,再推导并还原得到区间灰数的发展带预测模型;最后,将模型用于摆动幅度大且整体趋势增长的区间灰数在未来时刻的预测,预测效果验证了所提出模型的有效性.     

基于区间灰关联度的飞机战术意图识别方法

针对敌机在某一时刻其状态属性特征具有迷惑性的特点将某时间段敌机的特征指标变化序列用区间数表示,然后将敌机各类战术意图基准特征值也用区间数表示,将两类区间数都进行规范化处理,求得目标特征区间值与各类意图基准特征区间值的距离矩阵,用AHP方法确定特征指标权重,然后提出了一个新的灰关联模型并对其满足灰关联四公理进行了证明,用提出的灰关联度模型对目标战术意图进行识别.仿真结果证明区间灰关联度方法用于飞机战术意图识别的有效性,同时可以发现其在实时性方面的优势.     

A novel multivariate grey model for forecasting the sequence of ternary interval numbers

A novel multivariate grey model suitable for the sequence of ternary interval numbers is presented in the paper. New model takes into account the influencing factors on the system behavior characteristic. New parameter setting makes the model directly applicable to the sequence of ternary interval number without the need to convert the sequence into real sequence. A compensation coefficient taken as a ternary interval number is added to the model equation. The accumulation method based on the new information priority is proposed to estimate coefficients. A connotative prediction formula is derived to replace the white response equation of the classical multivariate grey model. The single variable grey model, which takes into account the development trend of system behavior itself, is combined with the novel multivariate grey model based on the degree of grey incidence. Interval forecasts for China's electricity generation and consumer price index show that the new model has good performance.     

The novel fractional discrete multivariate grey system model and its applications

Fractional order accumulation is a novel and popular tool which is efficient to improve accuracy of the grey models. However, most existing grey models with fractional order accumulation are all developed on the conventional methodology of grey models, which may be inaccurate in the applications. In this paper an existing fractional multivariate grey model with convolution integral is proved to be a biased model, and then a novel fractional discrete multivariate grey model based on discrete modelling technique is proposed, which is proved to be an unbiased model with mathematical analysis and stochastic testing. An algorithm based on the Grey Wolf Optimizer is introduced to optimize the fractional order of the proposed model. Four real world case studies with updated data sets are executed to assess the effectiveness of the proposed model in comparison with nine existing multivariate grey models. The results show that the Grey Wolf Optimizer-based algorithm is very efficient to optimize the fractional order of the proposed model, and the proposed model outperforms other nine models in the all the real world case studies.     

Grey forecasting method of quarterly hydropower production in China based on a data grouping approach

Grey model GM (1,1) has been widely used in short-term prediction of energy production and consumption due to its advantages in data sets with small numbers of samples. However, the existing GM (1,1) modelling method can merely forecast the general trend of a time series but fails to identify and predicts the seasonal fluctuations. In the research, the authors propose a data grouping approach based grey modelling method DGGM (1,1) to predict quarterly hydropower production in China. Firstly, the proposed method is used to divide an entire quarterly time series into four groups, each of which contains only time series data within the same quarter. Afterwards, by using the new series of four quarters, models are established, each of which includes specific seasonal characteristics. Finally, according to the chronological order, the prediction results of four GM (1,1) models are combined into a complete quarterly time series to reflect seasonal differences. The mean absolute percent errors (MAPEs) of the test set 2011Q1–2015Q4 solved using the DGGM (1,1), traditional GM (1,1), and SARIMA models are 16.2%, 22.1%, and 22.2%, respectively; the results indicated that DGGM (1,1) has better adaptability and offers a higher prediction accuracy. It is predicted that China's hydropower production from 2016 to 2020 is supposed to maintain its seasonal growth with the third and first quarters showing the highest and lowest productions, respectively.     

Forecasting energy demand using neural-network-based grey residual modification models

Grey forecasting models have taken an important role for forecasting energy demand, particularly the GM(1,1) model, because they are able to construct a forecasting model using a limited samples without statistical assumptions. To improve prediction accuracy of a GM(1,1) model, its predicted values are often adjusted by establishing a residual GM(1,1) model, which together form a grey residual modification model. Two main issues should be considered: the sign estimation for a predicted residual and the way the two models are constructed. Previous studies have concentrated on the former issue. However, since both models are usually established in the traditional manner, which is dependent on a specific parameter that is not easily determined, this paper focuses on the latter issue, incorporating the neural-network-based GM(1,1) model into a residual modification model to resolve the drawback. Prediction accuracies of the proposed neural-network-based prediction models were verified using real power and energy demand cases. Experimental results verify that the proposed prediction models perform well in comparison with original ones.     

基于均衡接近度的不确定型的统计决策方法及其应用

灰熵是离散序列均衡程度的测度,灰色关联度是参考序列和比较序列之间接近程度的测度.该方法将灰色关联度和均衡度合成为均衡接近度,并以此作为决策准则,提出了一种不确定型的统计决策的量化计算方法,从而避免了低层次多因素权重确定的主观性,使决策模型更加合理.通过将该决策方法在华油燃气公司决策中进行应用,为该公司选择了最优决策方案,使其获得了较好的经济效益.     

Discrete grey forecasting model and its optimization

Although the grey forecasting model has been successfully adopted in various fields and demonstrated promising results, the literatures show its performance could be further improved. For this purpose, this paper proposes a novel discrete grey forecasting model termed DGM model and a series of optimized models of DGM. This paper modifies the algorithm of GM(1, 1) model to enhance the tendency catching ability. The relationship between the two models and the forecasting precision of DGM model based on the pure index sequence is discussed. And further studies on three basic forms and three optimized forms of DGM model are also discussed. As shown in the results, the proposed model and its optimized models can increase the prediction accuracy. When the system is stable approximately, DGM model and the optimized models can effectively predict the developing system. This work contributes significantly to improve grey forecasting theory and proposes more novel grey forecasting models.     

硝苯地平缓释片释放过程的灰色数学模型

采用高分子材料制成硝苯地平缓释片 ,测定其释放度 ,以灰色数学模型进行分析 .结果表明 ,用灰色数学模型对药物释放过程进行拟合 ,拟合精度高 ,结果可靠 .灰色数学模型可用于描述其释药行为 .