analysis and application of demand forecasting model with multi element variable parameters

A forecasting model for logistics demand was presented to overcome the limitations of single goal forecasts of logistics demand and forecast data complexity. Based on the forecasting evaluation index and pretreatment of rough set theory, a multi input and multi output wavelet network (MMWNN) model for forecasting multi element regional logistics demand was studied. The network configuration was confirmed using the stepwise checkout and iterative gradient descent methods. After rough set reduction, the evaluation index was used to forecast the multi element regional logistics demand. The results of the numerical example indicate the feasibility and effectiveness of the model.     

The application of rough sets-neural network theory to mine ventilation system evaluation

To solve the instability problem of established sample in the neural network evaluation method for mine ventilation system, a comprehensive evaluation of the ventilation system is carried out based on rough sets and BP neural networks. Taking the ventilation system of a mine as an example, the classification quality of raw data samples are tested by using rough set data analysis system. Then, based on artificial neural network theory, a rough sets-neural network evaluation model of a mine ventilation system is established and a new rough sets-neural network evaluation method of mine ventilation system is formed. The results show that, after the model validation of data and application, its theoretical evaluation results are in line with the actual situation, and the network total error is less than 0.004. It shows that the comprehensive evaluation method based on rough sets-neural networks has a good effect in evaluating mine ventilation system in practical application.     

Knowledge Acquisition Approach Based on Fuzzy-rough Set

A new method of knowledge acquisition based on fuzzy - rough set is proposed. The continuous attributes in the decision table are fuzzified with the fuzzy membership functions. Lower and upper approximation of fuzzy - rough set are obtained by defining fuzzy equivalence relation. According to them the decision rules are acquired. At last, an example is illustrated and proves that the approach is effective.     

农村饮水安全定量评价方法研究综述

旨在明确农村饮水安全评价指标的具体含义,并对现有的定量评价方法进行论述。农村饮水安全问题具有模糊性、动态性及广泛性等特征。综合评价区域农村饮水安全现状需将水量、水质、方便程度及保证率4项指标进一步细化和规范化,从而建立一套完整的、符合地方实际的评价指标体系。指标权重值的确定是农村饮水安全定量评价的重难点。权重确定方法可划分为主观赋权法、客观赋权法及主客观组合赋权法。主观赋权法和客观赋权法均存在农村饮水安全多指标综合评价中,具有各自的优势及局限性,实现主客观赋权方法的优化组合,是确定农村饮水安全评价指标权重值的主要研究方向之一。     

基于logistic和灾减率方法制作玉米产量的预测

利用多元回归预报模型与灾减率相结合探讨粮食产量预报方法。依据本溪县玉米单产和气候资料,利用logistic方法建立玉米趋势产量序列,将分离的气象产量转换为相对气象产量,进行相关筛选预报因子,组建预测模型。结果表明：运用logistic方法构建的玉米趋势产量序列,提高了趋势产量拟合的精度;选取气象因子具有一定的生物学意义,增强了预测模型的科学性;多元回归预测模型与灾减率订正预测玉米单产正确率达90%。为粮食产量预报的定量化和精细化提供科学的依据。     

Application of partial least squares regression in the forecast of ground subsidence

Taking into account many influence factors of ground subsidence induced by underground exploitation,based on partial least squares multinomial regression,a forecast analysis on the maximum of ground subsidence is carried out.Taking height,depth,obliquity of coal clay and rigidity coefficient as independent variables,and maximum of ground subsidence as dependent variable,the forecast model of maximum of ground subsidence is obtained.It is found that,Press residual value decreases with the increase of number of latent variables,and the number of latent variables is four by Press residual value versus number of latent variables.The normal regression coefficient of height is the largest in the four influence factors,and this indicates that the influence of height is the largest on maximum of ground subsidence.The determination coefficient of forecast model obtained in this paper is 0.915 7,the error of forecast model is ±10.41%.The following conclusion can be drawn that the model based on partial least squares multinomial regression is a better and feasible non linear method.     

Cost forecast model for power engineering based on based on back propagation neural networks

A novel power engineering cost forecast model was proposed by combining feature extraction and small sample learning. The initial data was preprocessed with principal component analysis to remove the correlation among the original indexes and get the potential independent indexes. The new indexes acted as the input set to build a new forecast model based on least squares support vector machines. The results of this model were compared with the forecast results getting from artificial neural network. By comparing the forecast results with different principal components number, the optimal number was determined to achieve the desired forecast effect. The prediction results indicate that the method can extract the feature of initial data effectively and is good at small sample learning . The expected forecasting results can be reached.     

Fisher discriminant analysis model for selecting the retaining structure type of deep foundation pit

Aiming at the problem of traditional evaluation methods of deep foundation pit for selecting the retaining structure type, based on the statistical theory and following the principle of security, economic and reasonable, a Fisher discriminant analysis(FDA) model for selecting the retaining structure type for deep foundation pit is established. 10 selected indicators which influence selection of deep excavation program are taken into account as discriminant factors, and the supporting schemes for deep foundation pit are classified into 5 groups, viz. gravity of the cement-soil type, soil nailing wall, pile anchors, pile supports and underground continuous wall. After training and testing 64 sets of measured data, the discriminant functions of FDA are solved, the re-substitution method is introduced to verify the stability of FDA model and the ratio of mis-discrimination is 14.1%. Another 10 groups of measured data are tested as forecast samples by the proposed model, and the correct rate is equal to 100%. Therefore, the feasibility of the proposed model is validated. Moreover, the proposed model is adopted for the New World Center Project in China, and the prediction results are in line with the artificial neural network(ANN) and the actual situation. The result shows that the deep foundation pit supporting structure lectotype decision of FDA model has excellent discriminant performance and the resubstitution error rate is low. It is easy and efficient to make discriminant analysis using this model and it provides efficient method to select deep excavation retaining structure and a practical new approach to choose the structural type of deep foundation pit optimization.     

农业气象灾害综合评价中权重确定方法的研究

摘 要 福建省气象灾害种类繁多,但对农业生产的影响程度并不相同,只有通过权重的科学确定才能正确评价农业气象综合灾情。如何确定灾害权重,是进行农业气象灾害综合评价的核心问题。本文将主、客观赋权方法（层次分析法、灰色关联分析和熵权法）应用于福建农业气象灾害综合评价中权重的确定,探讨和比较它们在计算气象灾害权重方面的适用性,并在此基础上提出同时体现主观和客观信息的组合赋权法,得到福建农业气象灾害的组合权重,由大到小依次为低温冻害、旱灾、洪涝、风雹。结果表明,由组合赋权法计算的各灾害指标组合权重相比任何一种主、客观方法都更为合理和可靠。     

Direct generation of rough cut numerical control code from industrial CT slice images

This paper proposes a new method of generating a rough cut Numerical Control(NC) codes directly from industrial computerized tomography(CT) slice images for the products which work-piece did not need to be redesigned.The contour structure data of the work-piece are obtained by the threshold segmentation,edge extraction and contour tracing. The inner and outer contours are judged,and the tool radius compensation and the allowance reservation are performed by offsetting the contours according to the judgments. The authors generate the rough cut NC code cut area distinguishing and rough cut path generating in a slice by slice manner. In contour offsetting step, a new approach is proposed to judge inner and outer contours. A software system is developed to generate NC codes. An example is used to verify this approach and results show the feasibility and validity of the approach.     

北京地区春季木本植物花粉起始期预报模型研究

为解决城市花粉期预报的问题,以北京城区春季榆科、柏科、杨柳科、松科4类主要的木本植物为研究对象,利用2012—2019年北京市气象局的花粉浓度观测数据,分别基于Spring Warming、Alternating、Sequential和Parallel 4种积温物候模型进行植物花粉期建模。模型结果表明,4类木本植物的花粉期起始日期均明显受到春化作用影响,即冬季的低温刺激会促使春季植物花粉期的提前。对比不同模型的结果认为,榆科、柏科、松科植物的最适模型为Alternating模型,RMSE介于1～3天,判定系数R2介于91%～95%;杨柳科植物的最适模型为Sequential模型,RMSE小于2天,判定系数R2为92.8%。说明基于积温原理的物候模型,能够较为准确地模拟北京地区春季木本植物的花粉起始期,对于植物物候期的模拟研究具有参考意义。     

局域法邻近点选取对供水量预测精度的影响

混沌局域法预测模型适用于非线性、非平稳的城市日供水量预测,而邻近相点个数的选取对该模型预测精度有直接影响。传统方法通常以嵌入维m作为参考值,凭经验选取m+1个邻近相点,且仅使用欧式距离法计算当前相点距离,无法反映相点的运动趋势,易引入伪邻近相点,导致预测精度的降低。鉴于此,将演化追踪法引入城市日供水量预测,通过挖掘邻近相点的历史演化规律对参考样本进行优选,以提高预测精度。最后,采用实际日供水量数据验证所提出方法,结果表明,运用演化追踪法优选邻近相点能显著提高日供水量预测精度,预测平均绝对误差由2.501%降低到1.683%。     

ECMWF细网格对浙江沿海10 m风预报性能评估

为更好地开展浙江沿海海上大风的预报服务,本研究对2012年1月1日—2014年12月31日的ECMWF细网格10 m风场产品在浙江沿海的预报性能进行评估,并将其插值到沿海站点和观测数据进行对比分析。结果表明:冷空气影响下,浙江沿海的平均误差为负值,绝对误差分布和变化趋势与平均误差基本一致;台风影响下,24 h预报时效的平均误差为正值,随着预报时效的增加,平均误差逐渐转为负值,鱼山渔场、温台渔场及舟外渔场的绝对误差较其他区域大。另外,预报值和观测数据间的相关系数随预报时效的增加而减小,两者之间的相关系数平均值随海拔高度的增加而减小;ECMWF细网格对岱山和龙山村的预报偏大,对浪岗的预报偏小,预报偏差的离散度随预报时效增加而增大。     

基于高光谱的水稻土有机质含量估算研究

利用高光谱技术可实现土壤有机质含量的快速、精确反演。然而运用不同的光谱预处理算法及建模方法获取的模型预测精度及稳定性不同。为了选取最佳土壤有机质估算模型,本研究应用ASD波谱仪测定河南省潢川县水稻土的光谱数据,比较使用2种建模方法组合18种光谱预处理转换算法建立模型的反演效果。对于多元逐步回归模型和偏最小二乘模型,使用SGF3-2预处理算法均获得了最佳的预测效果,所建模型具有较小的误差和较高的精度。相比使用多元逐步回归法,使用偏最小二乘回归法可以获取更稳定的预测模型。运用偏最小二乘模型结合SGF3-2预处理算法得到了最佳的水稻土有机质含量估算模型,模型预测均方根误差RMSEv=0.036,决定系数Rv2=0.89。选择最佳的建模方法结合预处理算法,可以改进模型反演精度。本研究对比的不同方法也可以应用到类似的土壤模型选取中。     

重庆武隆高山甘蓝根肿病气象预测研究

根肿病是由芸薹根肿菌(Plasmodiophora brassicae Woron.)引起的一种世界性病害。任何十字花科植物的感病品种都会被该病菌侵染,在环境条件有利于病害发生时可导致作物产量和品质下降。为防御和减轻重庆地区高山甘蓝根肿病的发生,选择武隆高山甘蓝为研究对象,采用该区2010-2014年气象资料和高山甘蓝田间试验资料,分析气象因子与根肿病发病率的关系。筛选出对根肿病影响显著的气象因子为定植-成活期的累积雨量、定植后第12-14日的平均气温,采用回归方法建立甘蓝根肿病气象预测模型。利用2015-2016年试验资料对模型进行验证和试用,确定系数R2为0.68,说明该预测方法具有较好的客观性和适用性,可应用于重庆高山地区甘蓝根肿病发生的气象条件预测。气象防治和生物防治相结合,将为高山甘蓝根肿病的综合防治提供新的理论依据。     

Pollen-based predictive modelling of wine production: application to an arid region

A wine forecast model for one of the most arid wine regions of the Europe—Alentejo was improved and tested for the period 1998–2014. During this period, Alentejo region had strong upward trends in wine production associated to the increase of vineyard area. The forecast model was supported on a hierarchical analysis, including the determination of the potential production at flowering by quantifying airborne pollen concentration, followed by a climate based evaluation of the possible impact of fruit-set conditions in the limitation of production. Through the monitoring of airborne pollen flows it is possible to define an accurate main pollen season and determine the regional pollen index that will be used as independent variable in the regional forecast model. The time trend, which was initially removed from data, was then added back to obtain the forecast. Stepwise regression and cross-validation were employed during the period 1998–2014 for calibration of the model used for predicting annual wine production. The developed model explained about 86% of wine variance over the years with absolute average error of 6% for the cross validation and 87% of cases had differences between actual and forecasted wine production below 10%. The reliability and early-indication ability of the proposed forecast model justify their use to respond to a number of government agencies and wine industry concerns and activities.     

基于数据信息特征的土地资源评价客观赋权方法的研究

目前土地资源评价客观赋权方法种类繁多,而各种赋权方法对同一组指标数据的赋权结果不同,就常见客观赋权方法进行整理和归类,并以甘肃省庄浪县农村居民点整理数据为材料,从客观赋权方法原理出发,基于数据信息特征,引入变异系数、方差膨胀因子,根据指标体系中变异系数均值、方差膨胀因子均值的不同,创造性地构建差异组和相关组,在各指标体系下对赋权方法比较分析。结果表明：（1）利用差异信息赋权的熵权法、变异系数法、均方差法、离差最大化法,熵权法最优;（2）利用差异信息赋权的熵权法和利用重复信息赋权的复相关系数法的赋权结果无趋同性;（3）基于熵权法和复相关系数法的一种组合优化方法。     

A new integrating forecast and its application in short-term climate prediction〖JP〗

The existing problems of the traditional weight integrating forecast methods and the application in climate prediction are analyzed. A new method based on data mining is presented, which uses BP artificial neural network to build the integrating forecast classifier to integrate the forecast results of sub-methods. According to the features of different forecast objects, this method can change weight dynamically, which overcomes the shortage of the traditional weight integrating forecasts that cannot change weight after been decided and overcomes the shortage that cannot get the optimal results. By predicting the precipitation and average temperature of Chengkou County in January, and spring drought index of Chongqing from 2001 to 2007, the experiment results show that the reliability and accuracy of the proposed model are better than those of the sub-methods and other integrating forecast methods, which proves the effectiveness of this method.     

基于冠层平行平面光谱特征的冬小麦籽粒蛋白质含量预测

通过2个小麦品种,4个氮素水平的大田小区试验,及光谱仪传感器在冬小麦群体侧面水平测定不同叶层反射光谱,分析不同叶层光谱特征参量与冠层氮素分布、籽粒蛋白质含量的定量关系,建立籽粒蛋白质含量预测的分层光谱模型。结果表明,2个小麦品种冠层内氮素分布特点不同,普通蛋白质含量小麦京冬8号上、下叶层对不同氮素处理反     

Application of Combination Grey Model in Power Load Forecasting

Based on the gray forecast theory, this paper studies the principle and deficiency in power load forecasting by the basic grey model and other improved models, and introduces a new method -the combination grey model to forecast the long-medium power load. Based on an example, the basic grey model, other improved models and combination grey model are used to forecast power load and results of all models are analyzed and compared. The calculation results show that forecasting power load by grey theory is credible and simple. For this type of complex problems such as forecasting the long-medium power load, the combination grey model is specially useful because of it's high precision and facility. The method can be used as one of the tools of forecasting the long-medium power load.