Application of Gray Metabolic GM (1,1) Model in Prediction of Annual Total Yields of Chinese Aquatic Products

To predict the annual total yields of Chinese aquatic products in future five years ( 2011-2015) ,based on the theory and method of gray system,this paper firstly establishes a conventional GM ( 1,1) model and a gray metabolic GM ( 1,1) model respectively to predict the annual total yields of Chinese aquatic products in 2006-2009 and compare the prediction accuracy between these two models. Then,it selects the model with higher accuracy to predict the annual total yields of Chinese aquatic products in future five years. The comparison indicates that gray metabolic GM ( 1,1) model has higher prediction accuracy and smaller error,thus it is more suitable for prediction of annual total yields of aquatic products. Therefore,it adopts the gray metabolic GM ( 1,1) model to predict annual total yields of Chinese aquatic products in 2011-2015. The prediction results of annual total yields are 55. 32,57. 46,59. 72,62. 02 and 64. 43 million tons respectively in future five years with annual average increase rate of about 3. 7% ,much higher than the objective of 2. 2% specified in the Twelfth Five-Year Plan of the National Fishery Development ( 2011 to 2015) . The results of this research show that the gray metabolic GM ( 1,1) model is suitable for prediction of yields of aquatic products and the total yields of Chinese aquatic products in 2011-2015 will totally be able to realize the objective of the Twelfth Five-Year Plan.

GM(1,N)模型的病态性研究及其在生态创新中的应用

本文以中国工业企业为研究对象,深入探究适用于多因素、少数据的生态创新相关指标特征的灰色模型预测技术。针对传统灰色预测模型在进行参数估计时可能存在的病态性问题展开研究,通过引入L2正则项的最小二乘法,利用粒子群算法求解最优值。将该模型应用于生态创新,与其他模型进行结果对比。结果表明,引入L2正则项的最小二乘法解决了模型的病态性问题,具有良好的预测性能,验证了该模型的有效性。

基于改进GM(1,n)的动态网络舆情预警模型

舆情的自由传播会导致网络集群行为的发生,易产生负面社会影响,威胁公共安全,因此建立网络舆情监控及预警机制是防控舆情传播、维护社会稳定的必要措施。首先,通过分析谣言的形成机制,构建了舆情发展预测指标体系;其次,通过建立多因素GM(1,n)模型对舆情发展的走向进行预测;然后,分别结合新陈代谢理论与马尔可夫理论改进上述预测模型;最后,以微博“新疆棉”事件和“成都四十九中”事件为例,对GM(1,n)模型、马尔可夫GM(1,n)模型和新陈代谢马尔可夫GM(1,n)模型预测舆情发展的能力进行对比,并比较了新陈代谢马尔可夫GM(1,n)模型与随机森林模型。实验结果表明,相较于原始模型与随机森林模型,新陈代谢马尔可夫GM(1,n)模型的平均预测精度分别提高了10.6和5.8%。可见,新陈代谢马尔可夫GM(1,n)模型在预测网络舆情发展趋势问题上具有良好的性能。

Study on the Prediction of Rice Blast Based on the Unbiased GM (1,1) Model

To create a new prediction model, the unbiased GM （1,1） model is optimized by the five-point slide method in this paper. Then, based on the occurrence areas of dce blast in Enshi District during 1995 -2004, the new model and unbiased GM （1, 1 ） model are applied to predict the occurrence areas of rice blast during 2005 -2010. Predicting outcomes show that the prediction accuracy of five-point unbiased sliding optimized GM （1, 1 ） model is higher than the unbiased GM （1,1） model. Finally, combined with the prediction results, the author provides some suggestion for Enshi District in the prevention and control of rice blast in 2010.

基于新陈代谢算法优化的GM(1,N)动态网络灰分拟合

煤炭是我国能源资源安全的压舱石,“双碳”背景下实现煤炭清洁加工与高效利用意义重大,而灰分检测对煤炭清洁化和智能化发展尤为重要。针对现有灰分检测存在的检测精度有待提高的突出问题,以两淮矿区典型煤样为研究对象,通过慢灰和X射线荧光(X ray fluorescence,XRF)测试系统地探究了煤样的灰分和元素组成分布规律,并结合机器学习理论构建了灰分-元素特征数据集;结合灰色系统理论和新陈代谢算法,构建了自适应的GM(1,N)动态网络灰分拟合优化模型,并详细设计了动态网络算法流程;提出了GM(1,N)动态模型的关键超参数,并通过与常规拟合方法对比,全面地评价了模型拟合性能。结果表明:两淮矿区煤可视为由可燃元素和成灰元素共同构成,且成灰元素中质量分数占比最高为Si和Al,次之为S、Fe和Ca等,最少为P和Cl等,并且煤中成灰元素总含量与灰分呈正相关,而可燃元素与之相反;以灰分为标签值、以组成元素为特征值,形成了煤的灰分-元素特征数据集;以样本数据划分→动态网络灰分拟合→模型评价机制→动态拟合模型自适应优化→鲁棒性提升→多轮迭代优化为主线设计了GM(1,N)动态网络灰分拟合模型及其算法流程,有效提升了数据集稳定性和新鲜度,并且迭代收敛速度快,灰分误差阈值5%时其准确率达100%;对比经典GM(1,N)模型和常规多元线性回归模型,证明了新模型的灰分拟合性能得到显著提升,其相对误差为0.16%~4.96%、误差均值仅2.29%。

血清CHI3L1水平与2型糖尿病合并代谢相关脂肪性肝病患者的相关性分析

目的 探讨血清壳多糖酶3样蛋白1(CHI3L1)水平与2型糖尿病(T2DM)患者合并代谢相关脂肪性肝病(MAFLD)的关系。方法 按照1∶1比例选取2020年1月至2023年12月河北省唐山南湖医院收治的T2DM合合并MAFLD患者(MAFLD组)和未合并MAFLD的T2DM患者(非MAFLD组)各120例,采用酶联免疫吸附法检测2组患者血清CHI3L1水平,并收集2组患者基线资料。采用多因素条件Logistic回归模型分析CHI3L1与T2DM患者合并MAFLD的关系,绘制受试者工作特征(ROC)曲线,分析CHI3L1对T2DM合并MAFLD的预测价值。结果 与非MAFLD组比较,MAFLD组血清CHI3L1水平升高(P <0.05)。调整混杂因素后,CHI3L1升高为T2DM患者合并MAFLD的独立危险因素(P <0.05)。血清CHI3L1水平预测T2DM患者合并MAFLD的曲线下面积(AUC)为0.803,敏感度为70.83%,特异度为88.33%。结论 T2DM患者血清CHI3L1水平升高是合并MAFLD的独立危险因素,并对T2DM患者合并MAFLD具有一定的预测价值。

Prediction of efficient outputs based on GM(1,N) model and weak DEA efficiency

This paper expresses the efficient outputs of decisionmaking unit（DMU） as the sum of ＂average outputs＂ forecasted by a GM（1,N） model and ＂increased outputs＂ which reflect the difficulty to realize efficient outputs.The increased outputs are solved by linear programming using data envelopment analysis efficiency theories,wherein a new sample is introduced whose inputs are equal to the budget in the issue No.n ＋ 1 and outputs are forecasted by the GM（1,N） model.The shortcoming in the existing methods that the forecasted efficient outputs may be less than the possible actual outputs according to developing trends of input-output rate in the periods of pre-n is overcome.The new prediction method provides decision-makers with more decisionmaking information,and the initial conditions are easy to be given.

Application of Renewal Gray GM (1,1) Model to Prediction of Landslide Deformation

The gray renewal GM (1,1) landslide prediction model was established by improving the gray model. Based on the established model, the author has made prediction of landslide deformation to the Xiangjiapo landslide and the Lianziya dangerous rock body. The results show that the gray renewal GM (1,1) model can supplement the new information in time and remove the old information which reduces the meaning of the information because of time lapse. Therefore, the model is closer to reality.

Construction of GM(2,1)Model for Oscillating Pitch Angle Series

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 Modified GM( 1 , 1) Grey Forecast Model

Because the impacts of the factors such as some disturbances are graduallyadded into the system, the grey forecast results will deviate from the systemtrue value. To improve the forecast precision, Pro-Dens Julons provided twomethfor-But they had not consider the impact of artificial disturbance. LiZhihua et al. of Qinghua Univ. presented another method. This paper revisesthe method and make it be a spocial case.

<i>In Silico</i>Modeling of C1 Metabolism

An integrative computational, in silico, model of C1 metabolism is developed from molecular pathway systems identified from a recent, comprehensive systematic bioinformatics review of C1 metabolism. C1 metabolism is essential for all organisms to provide one-carbon units for methylation and other types of modifications, as well as for nucleic acid, amino acid, and other biomolecule syntheses. C1 metabolism consists of three important molecular pathway systems: 1) methionine biosynthesis, 2) methylation cycle, and 3) formaldehyde detoxification. Each of the three molecular pathway systems is individually modeled using the CytoSolve?? Collaboratory?, a proven and scalable computational systems biology platform for in silico modeling of complex molecular pathway systems. The individual models predict the temporal behavior of formaldehyde, formate, sarcosine, glutathione (GSH), and many other key biomolecules involved in C1 metabolism, which may be hard to measure experimentally. The individual models are then coupled and integrated dynamically using CytoSolve to produce, to the authors’ knowledge, the first comprehensive computational model of C1 metabolism. In silico modeling of the individual and integrated C1 metabolism models enables the identification of the most sensitive parameters involved in the detoxification of formaldehyde. This integrative model of C1 metabolism, giving its systems-based nature, can likely serve as a platform for: 1) generalized research and study of C1 metabolism, 2) hypothesis generation that motivates focused and specific in vitro and in vivo testing in perhaps a more efficient manner, 3) expanding a systems biology understanding of plant bio-molecular systems by integrating other known molecular pathway systems associated with C1 metabolism, and 4) exploring and testing the potential effects of exogenous inputs on the C1 metabolism system.

基于PSR和无偏GM(1,1)模型的福建省耕地生态安全评价与预测

研究目的:对福建省2004—2015年的耕地生态安全状况进行评价,并对2016—2025年的安全状况进行预测,以指导区域耕地合理、可持续利用。研究方法:运用PSR评价模型建立评价指标体系、熵权法确定权重,采用无偏GM(1,1)模型进行预测,通过障碍度模型确定障碍因子。研究结果:(1)2004—2015年福建省耕地生态安全值呈曲折上升趋势,安全级别从较安全级上升为安全级;(2)预测结果显示,2016—2025年福建省耕地生态安全状况维持在安全状态,但安全值呈直线下降趋势;(3)通过障碍度模型得出未来10年福建省耕地生态安全的障碍因素主要集中在经济发展状况及农业生产活动方面。研究结论:2004—2015年,福建省的耕地生态安全状况总体较好,未来10年的耕地安全状况还需通过出台惠农政策、大力开展土地整治及宣传耕地生态安全知识等措施加强。

Predicting the Number of Beijing Science and Technology Personnel Based on GM(1,N) Model

In this paper, based on the Science and Technology Statistics in Beijing Statistical Yearbook, grey theory is used to study the relationship among S&T (Science and Technology) activities personnel, R&D (research and development) personnel FTE (Full Time Equivalent), intramural expenditure for R&D and Patent Application Amount. According to the grey correlation coefficient, screening of grey GM(1,N) prediction variables, the grey prediction model is established. Meanwhile, time series model and GM(1,1) model are established for patent applications and R&D personnel equivalent FTE. By comparing the simulating results with the real data, the absolute relative error of prediction models is less than 10%. The results of the prediction model are tested. In order to improve the prediction accuracy, the mean values of the predicted values of the two models are brought into the GM(1,N) model to predict the number of scientific and technical personnel in Beijing during 2015-2025. Forecast results show that the number of science and technology personnel in Beijing will grow with exponential growth trend in the next ten years, which has a certain reference value for predicting the science and technology activities and formulating the policy in Beijing.

灰色新陈代谢GM(1,1)模型在参考作物腾发量预测中的应用研究

随着时间的发展,任何一个灰系统都会加入一些未来的扰动因素,对系统的发展产生影响。而常规灰色GM(1,1)预测模型在建模时,采用的是现实时刻t=n为止的过去的数据,没有考虑这些扰动因素。故离现实时刻越远,该模型的预测精度就越低。针对这种情况,提出了灰色新陈代谢GM(1,1)预测模型。并尝试用灰色新陈代谢GM(1,1)预测模型对阜新市参考作物腾发量进行预测。结果表明:灰色新陈代谢GM(1,1)预测模型的预测精度较常规灰色GM(1,1)预测模型有了明显的提高。该模型建模过程简单、适用性强,为参考作物腾发量的预测提供了新的方法。

区间灰数表征与算法改进及其GM(1,1)模型应用研究

针对目前区间灰数的表征和运算过程中存在着较为严重的问题 ,即 :运算结果对灰数的灰度进行不必要的放大 ,造成信息的严重失真 ,笔者定义了标准区间灰数与第一和第二标准区间灰数的概念 ,分析了第一和第二标准区间灰数之间的关系 ,进一步设计了普通区间灰数与标准区间灰数之间的转换规则 ,提供了标准区间灰数之间的比较与运算法则 ,较好地解决了区间灰数之间的大小比较与运算问题。最后 ,将这一研究成果应用于基于区间数的GM (1,1)模型预测问题 ,取得了良好的效果。

GM(1，1)模型在铝电解氧化铝浓度估计中的应用研究

详细介绍了灰色GM(1,1)模型的原理及建立步骤。将GM(1,1)模型引入到铝电解氧化铝浓度估算中,对基于Kalman滤波的氧化铝浓度自适应估算模型进行修正。灰色模型建模维度为5,对采用浓度自适应估算模型得到的浓度与实际浓度的偏差序列进行预测,将预测值反馈到浓度自适应估算模型中,形成新的浓度估算值。通过对电解生产中的30余组氧化铝浓度工艺数据进行估算,结果表明,经修正后,浓度估算值与实际值变化趋势保持一致,基本能满足铝电解工业现场的应用要求。

非等间距GM(1,1)模型背景值的改进及其最优化

背景值构造方法是影响非等间距GM(1,1)模型精度和适应性的关键因素之一,文章分析了非等间距GM(1,1)模型中的背景值,提出了用Newton插值和数值积分中的Newton-Cores公式与Gauss-Legendre公式分别重构模型中的背景值。该背景值不仅适合于非等间距建模,也适合于等间距建模,该模型进一步拓广了GM(1,1)的适应范围,数据模拟结果充分说明了新模型的有效性和优越性。

色谱灰色数模的建立及其应用研究(Ⅰ)──连续GM(1,N)模型的建立及色谱保留行为的预测

应用灰色系统理论和最小二乘原理，推导出连续GM（1，N）模型，并将该模型用于高效液相色谱中溶质保留行为的预测．研究了烷烃、烷基苯及对羟基苯甲酸酯等化合物的容量因子与流动相组成、容量因子与碳数、保留时间与温度、保留时间与碳数的关系，并建立了相应的数学模型，经验证，预测结果良好．

改进GM(1,1)模型在路基沉降预测中的应用

GM(1,1)模型是沉降预测中应用较为广泛的模型之一,在实际应用当中发现其预测效果有时候较差甚至完全失效。为此从传统 GM(1,1)预测模型建模原理出发,分析存在的理论缺陷,指出在形成预测公式时规定(?)^(1)(1)=x^(1)(1),α=0.5为已知条件是不合理的,应当根据实际情况选用其他数据。通过引入平均斜率来计算α(k)序列,并利用 MATLAB 程序进行数据处理,寻找最合适的(?)^(1)(1)以及最佳维数区。某高速公路实测沉降数据计算表明,新方法有满意的拟合和预测效果,为提高建模精度提供了新的途径。

基于灰色GM(1，1)非等时距修正模型的轨道质量预测

根据灰色理论,以轨道质量指数检测数据为原始时间序列,通过累加弱化序列的随机性,挖掘轨道系统内在的规律,研究建立基于灰色GM(1,1)非等时距模型的轨道质量预测方法。为提高模型预测精度,优化模型中的初值和背景值,并基于残差分析引入周期性函数,对模型进行修正。用此模型对轨道质量指数TQI数据进行分析预测,并对模型精度进行检验。结果表明模型能较好地反映轨道质量恶化发展的随机波动特征,拟合、预测精度高,为了解和掌握轨道质量状态的发展规律提供了新的方法。