Generalized weighted functional proportional mean combining forecasting model and its method of parameter estimation

A new kind of combining forecasting model based on the generalized weighted functional proportional mean is proposed and the parameter estimation method of its weighting coefficients by means of the algorithm of quadratic programming is given. This model has extensive representation. It is a new kind of aggregative method of group forecasting. By taking the suitable combining form of the forecasting models and seeking the optimal parameter, the optimal combining form can be obtained and the forecasting accuracy can be improved. The effectiveness of this model is demonstrated by an example.

Function S-rough sets and two law forecast

Function S-rough sets has the properties of dynamics, heredity, and memory. Function S-rough sets is penetrated and crossed with the issue of economic law forecast, then a new forecast model based on function S-rough sets namely the two law forecast model is proposed, which includes upper law forecast model and lower law forecast model; and its＇ implement algorithm is given. Finally, the validity of the model is demonstrated by the forecast for region economic development of Hainan Province.

Sensitivity Analysis of Radial Basis Function Networks for River Stage Forecasting

Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling has previously been limited to exploring perturbations to both inputs and connecting weights. In this paper, the backward chaining rule that has been used for sensitivity analysis of MLPs, is applied to RBFNs and it is shown how such analysis can provide insight into physical relationships. A trigonometric example is first presented to show the effectiveness and accuracy of this approach for first order derivatives alongside a comparison of the results with an equivalent MLP. The paper presents a real-world application in the modelling of river stage shows the importance of such approaches helping to justify and select such models.

Average Power Function of Noise and Its Applications in Seasonal Time Series Modeling and Forecasting

This paper presents a new method of detecting multi-periodicities in a seasonal time series. Conventional methods such as the average power spectrum or the autocorrelation function plot have been used in detecting multiple periodicities. However, there are numerous cases where those methods either fail, or lead to incorrectly detected periods. This, in turn in applications, produces improper models and results in larger forecasting errors. There is a strong need for a new approach to detecting multi-periodicities. This paper tends to fill this gap by proposing a new method which relies on a mathematical instrument, called the Average Power Function of Noise (APFN) of a time series. APFN has a prominent property that it has a strict local minimum at each period of the time series. This characteristic helps one in detecting periods in time series. Unlike the power spectrum method where it is assumed that the time series is composed of sinusoidal functions of different frequencies, in APFN it is assumed that the time series is periodic, the unique and a much weaker assumption. Therefore, this new instrument is expected to be more powerful in multi-periodicity detection than both the autocorrelation function plot and the average power spectrum. Properties of APFN and applications of the new method in periodicity detection and in forecasting are presented.

变形监测灰色预测模型对比及替代方法研究

将变形监测灰色预测模型分为传统GM(1,1)模型及其改进模型、非齐次灰色模型、GM(1,1)幂模型及其改进模型3种类型,以Origin拟合函数Exp2PModl、Exponential和SRichards2作为3类灰色预测模型的替代方法,基于理论研究和实例验证对比分析3类灰色预测模型及其替代方法。结果表明,3类灰色预测模型在拟合函数、有无极限值、适合等时距或非等时距建模和适用范围等方面存在显著差异,需要根据变形监测数据特征选择合适的灰色预测模型类别;与3类灰色预测模型相比,Origin拟合函数在参数求解和建模数据要求上更具优势,而且可以得到相当甚至更高的拟合或预测精度,除需要编程实现的特殊优化目标外,完全可以代替灰色预测模型用于变形监测。

计及预测误差时变相关特性的新型电力系统爬坡容量需求分析方法

为提高新型电力系统爬坡容量需求分析的准确性,提出了计及净负荷预测误差时变相关特性的爬坡容量需求分析方法。首先明确新型电力系统所需要的爬坡容量构成,分析爬坡需求与净负荷预测误差之间的关系。然后,建立了净负荷预测误差与净负荷预测值的动态Copula函数,通过演进方程构建Copula函数参数的时序关联关系,应用条件概率理论建立由于净负荷预测误差而产生的爬坡需求概率分布模型,以置信区间对爬坡需求概率分布结果进行离散化表征,从而得到相应的爬坡容量需求。最后,基于我国西部某电网的实际运行数据,以是否考虑预测误差时变相关特性构建了3种不同的测算模型,算例计算结果验证了所提方法的有效性和正确性。

基于GA-BP神经网络模型城市河道水位预报研究

城市内河水位预报对城市内涝风险管理具有重要意义。传统数值模拟模型计算效率较低,且无法实时计算。针对以上问题,提出一种基于Gaussian函数改进BP神经网络的河道水位预报模型,解决了BP神经网络模型预报精度低、在误差平坦区收敛速度慢的问题。该方法利用Gaussian函数改进BP神经网络梯度下降算法,针对模型不同权重与阈值设定不同学习率,对各参数进行针对性优化,能够有效加速BP神经网络模型训练效率;针对模型在误差平坦区收敛速度慢的问题,通过Gaussian函数增大梯度下降算法在误差平坦区的学习率,控制梯度下降算法在误差较大时的学习率,能够有效加速BP神经网络模型在误差平坦区的收敛速度。以福州市晋安区6个河道水位测站为研究对象,构建GABP神经网络河道水位预报模型进行城市内河水位预报,并探讨不同降雨输入形式对河道水位预报精度的影响。结果表明:GA-BP神经网络能够有效提升BP神经网络在误差平坦区的收敛速度与模型预报精度,试验集预报纳什效率系数(NSE)均在0.8以上,能够将预报峰值水位相对误差控制在5%以内,其中降雨以小时降雨量形式输入能够将预报NSE提升至0.9以上。研究表明采用Gaussian函数改进BP神经网络模型能够有效提升模型预报精度,对提升城市河道水位预报具有重要意义。

基于LSTM神经网络的基坑工程智能预警系统研发与应用

基坑开挖过程中伴随有支护结构及周围岩土体的受力和形变状态的改变,因此在工程建设中对基坑进行监测十分必要。为解决基坑监测智能化程度低、可视化程度低、预警精确度较低导致频繁报警、监测数据更新共享速度慢等问题,采用B/S模式、Vue前端、C#语言后端、SQLServer2012数据库等并嵌入python语言编写长短期记忆(Long Short-Term Memory,LSTM)神经网络算法模型开发一套基坑智能预测预警系统。该系统实现了信息集中管理、数据存储与查看、数据算法自动计算、自动绘制图表、自动报警预警、快速生成报警报告等功能。通过在苏州某地铁基坑开挖过程的应用,证明了本系统能够综合利用监测预警与基于LSTM神经网络模型的超前预测预警两种预警模式为施工人员准确掌握基坑开挖过程中支护结构及周围土体变形情况提供技术支持与保障,具有很强的现实使用意义。

基于网函数插值理论的结构表面风压预测

影响结构表面风荷载分布特性的因素很复杂,且具有不确定性,仅靠风洞试验难以完整地获得和描述其风荷载分布特性。因此,探讨了基于网函数插值理论的结构表面风压分布预测。首先,介绍了一种可多方向拟合一次误差调整的网函数插值法,并基于网函数插值技术对于多个传感器所获得的数据进行数据融合,提出了结构表面风压分布预测方法,然后用风洞试验数据进行了验证。实例验证结果表明:网函数插值法可以根据布置在结构物表面的测点网,快速计算出相邻位置的表面风压值,这是作为数据融合手段预测结构表面风压分布特性的一个有效途径,且该方法稳定性好,简单实用,精度较高,可被应用于风洞试验中减少测点数量和扩充复杂建筑表面风压分布数据等。

售电侧开放市场环境下基于多分位鲁棒极限学习机的短期负荷预测技术

该文基于极限学习机算法设计一种用于短期负荷预测的多分位鲁棒极限学习机模型,该模型能解决传统预测模型抗干扰能力差的缺陷,可以在面临不确定性因素干扰的情况下准确预测负荷。对传统模型和多分位鲁棒极限学习机模型的鲁棒性和多分位性进行验证,对比结果表明,多分位鲁棒极限学习机模型的鲁棒性更好,在不同分位下的预测精度更高。

复合型轨道交通车站交通接驳需求预测方法——以北京草桥站为例

由于服务客群的多样化,复合型轨道交通车站的接驳需求、接驳设施配置及布局原则等均与普通轨道交通车站存在差异.通过分析不同服务客群在出行特征及交通接驳需求上的差异,提出复合型车站的接驳需求预测方法.以北京草桥站为例,在其区位、功能定位、现状及规划条件的基础上,通过对大兴机场线服务的航空客流、19号线及10号线服务的普通客流的出行特征及接驳需求的分析,得到多类型客流下车站分方式接驳需求量及设施规模.最后,结合出行特征及用地条件对车站交通接驳设施进行设置与布局.

流域防汛智慧化总体框架建设及实施路径研究

为大力推进智慧水利和数字孪生流域建设,在防洪减灾领域需要构建流域防汛智慧化总体框架并研究实施路径,进而推动新阶段水利高质量发展。分析流域防汛智慧化建设过程中涉及的防汛业务应用智能化水平和应用系统建设水平等现状,从打造智慧化防洪“四预”体系、建设“三算”能力提升体系等方面构建我国防汛智慧化总体框架,并从优化提升洪水监测预报能力、加强洪水模拟推演和防洪调度模拟仿真技术应用、提升洪水风险管理与科学精准决策能力等方面提出具体实施路径。研究成果能为水利部门开展智慧化流域防洪业务、流域防洪规划修编提供技术支撑和对策参考。

Application test of matter element analysis in earthquake forecast

Calculation by means of the previous indices of the seismic activity can have the matter element analysis possess the forecast function. Readjusting repeatedly the grade limit value of every index can maximize the historical fitting ratio of the calculated and actual grade of the annual maximum magnitude, whose result is relatively ideal.

TRIZ TECHNOLOGY FORECASTING AS QFD INPUT WITHIN THE NPD ACTIVITIES

As a result of the fierceness of business competition, companies, to remaincompetitive, have to charm their customers by anticipating their needs and being able to rapidlydevelop exciting new products for them. To overcome this challenge, technology forecasting isconsidered as a powerful tool in today's business environment, while there are as many successstories as there are failures, a good application of this method will give a good result. Amethodology of integration of patterns or lines of technology evolution in TRIZ parlance ispresented, which is also known as TRIZ technology forecasting, as input to the QFD process to designa new product. For this purpose, TRIZ technology forecasting, one of the TRIZ major tools, isdiscussed and some benefits compared to the traditional forecasting techniques are highlighted. Thena methodology to integrate TRIZ technology forecasting and QFD process is highlighted.

Traffic Flow Data Forecasting Based on Interval Type-2 Fuzzy Sets Theory

This paper proposes a long-term forecasting scheme and implementation method based on the interval type-2 fuzzy sets theory for traffic flow data. The type-2 fuzzy sets have advantages in modeling uncertainties because their membership functions are fuzzy. The scheme includes traffic flow data preprocessing module, type-2 fuzzification operation module and long-term traffic flow data forecasting output module, in which the Interval Approach acts as the core algorithm. The central limit theorem is adopted to convert point data of mass traffic flow in some time range into interval data of the same time range(also called confidence interval data) which is being used as the input of interval approach. The confidence interval data retain the uncertainty and randomness of traffic flow, meanwhile reduce the influence of noise from the detection data. The proposed scheme gets not only the traffic flow forecasting result but also can show the possible range of traffic flow variation with high precision using upper and lower limit forecasting result. The effectiveness of the proposed scheme is verified using the actual sample application.

Quadratic Radical Function Better Than Fisher z Transformation

A new explicit quadratic radical function is found by numerical experiments,which is simpler and has only 70.778%of the maximal distance error compared with the Fisher z transformation.Furthermore,a piecewise function is constructed for the standard normal distribution:if the independent variable falls in the interval(-1.519,1.519),the proposed function is employed;otherwise,the Fisher z transformation is used.Compared with the Fisher z transformation,this piecewise function has only 38.206%of the total error.The new function is more exact to estimate the confidence intervals of Pearson product moment correlation coefficient and Dickinson best weights for the linear combination of forecasts.

血清胎盘生长因子和脑啡肽酶对老年心力衰竭患者心功能分级和心肌损伤的预测作用

目的探讨血清胎盘生长因子(PLGF)和脑啡肽酶表达水平在老年心力衰竭患者不同心功能分级中的变化,并评估其对心肌损伤的预测作用。方法选择2020年1月至2022年10月石河子大学医学院第一附属医院心内一科和心内二科收治的226例老年心力衰竭患者作为心力衰竭组(心衰组),根据心力衰竭程度分为心功能Ⅱ级75例、心功能Ⅲ级82例、心功能Ⅳ级69例,选择同期88例健康体检者作为对照组。观察PLGF、脑啡肽酶、心肌肌钙蛋白I(cTnI)水平及LVEF、左心室舒张末期内径(LVEDD)、舒张早期与舒张晚期二尖瓣血流峰值比值(E/A)。采用Pearson相关性分析PLGF和脑啡肽酶与心功能指标及cTnI的相关性。结果与对照组比较,心衰组脑啡肽酶、LVEF、E/A水平显著降低,PLGF、cTnI、LVEDD水平显著升高,差异有统计学意义(P<0.05);随着心功能分级增加,脑啡肽酶水平显著降低,PLGF、cTnI水平显著升高,差异有统计学意义(P<0.05)。Pearson相关性分析显示,心衰组血清脑啡肽酶、PLGF水平与cTnI分别呈明显负相关(r=-0.528,P=0.000)和正相关(r=0.591,P=0.000);脑啡肽酶、PLGF水平与LVEF呈明显正相关(r=0.621,P=0.000)和负相关(r=-0.688,P=0.000);脑啡肽酶、PLGF与LVEDD分别呈明显负相关(r=-0.698,P=0.000)和正相关(r=0.755,P=0.000);脑啡肽酶、PLGF与E/A分别呈明显正相关(r=0.774,P=0.000)和负相关(r=-0.763,P=0.000)。结论心力衰竭患者血清脑啡肽酶水平异常降低,血清PLGF水平异常升高,与心力衰竭程度和心功能之间存在明显相关性;两者均对老年心力衰竭患者心功能分级和心肌损伤具有重要预测作用,有望成为心力衰竭严重程度评估指标。

Modeling Breast Cancer Incidence Rates: A Comparison between the Components of Functional Time Series (FTS) Model Applied on Karachi (Pakistan) and US Data

Several studies showed that the breast cancer incidence rates are higher in high-income (developed) countries, due to the link of breast cancer with several risk factors and the presence of systematic screening policies. Some of the authors suggest that lower breast cancer incidence rates in low-income (developing) countries probably reflect international variation in hormonal factors and accessibility to early detection facilities. Recent studies showed that the breast cancer increased rapidly among women in Pakistan (a developing country) and it became the first malignancy among females of Pakistan. Although, the incidence rates may contain important evidence for understanding and control of the disease;however in Pakistan, the breast cancer incidence data have never been available in the last five decades since independence;rather, only hospital-based data are available. In this study, we intend to apply Functional Time Series (FTS) models to the breast cancer incidence rates of United State (developed country), and to see the difference between various components (age and time) of Functional Time Series (FTS) models applied independently on the breast cancer incidence rates of Karachi (Pakistan) and US. Past studies have already suggested that the incidence of US breast cancer cases was expected to increase in the coming decades. A progressive increase in the number of new cases is already predetermined by the high birth rate that occurred during the middle part of the century, and it will lead to nearly a doubling in the number of cases in about 4 decades. We also obtain 15 years predictions of breast cancer incidence rates in United States and compare them with the forecasts of incidence curves for Karachi. Development of methods for cancer incidence trend forecasting can provide a sound and accurate foundation for planning a comprehensive national strategy for optimal partitioning of research resources between the need for development of new treatments and the need for new research directed toward primary preventive measures.

基于M1DCNN-BiLSTM在铝合金桁架结构健康监测的应用研究

为克服传统结构健康监测方法在特征提取和时序建模方面的限制,提出一种高效的加速度差值预处理方法,将其应用于多尺度一维卷积神经网络(M1DCNN)与双向长短期记忆网络(BiLSTM)联合模型中,旨在深入研究和评估这一方法在铝合金桁架结构健康监测中的适用性。通过多层次的特征提取和时序建模,实现对振动信号的深层次时空特征表达。利用铝合金桁架的数值模拟和实际振动测试数据,对比不同模型在损伤位置识别和程度预测方面的性能。结果表明:与传统的1DCNN方法相比,M1DCNN-BiLSTM模型能有效识别桁架结构中不同位置的损伤,准确量化损伤的严重程度。该模型在损伤位置和程度预测方面表现出色,准确率和预测精度几乎达到100%。在振动测试实验中,其RMSE、MSE和MAE分别比1DCNN降低了84.5%、97%和84.3%,预测结果与实际损伤程度高度吻合。

基于Copula函数的叶片载荷相关性研究及预测分析

针对风速与叶片载荷的相关性,建立风速-载荷的Copula函数模型,提出一种基于Copula函数的预测方法并对叶片载荷进行预测。通过中小型变桨风力机风洞试验的载荷数据进行相关性建模并进行拟合优度检验,同时通过预测模型对载荷做出预测,采用显著性检验验证了预测效果的有效性。以1.5 kW风力机叶片载荷试验数据为例对所提方法验证,结果表明:在准确分析风速和叶片载荷相关性的基础上,采用Copula函数建立的预测模型能有效预测叶片载荷。