共查询到19条相似文献，搜索用时 540 毫秒
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以纯电动客车为研究对象,为其配备一台自动机械式变速器(AMT)以达到提高其续驶里程的目的。通过调整变速器机械机构及控制模型,提出了一种纯电动客车带自动机械式变速器的传动系统模型;通过整车性能指标确定了变速器的档位数和传动比范围;并以纯电动客车续驶里程为目标优化了传动比,从而确定了变速器的参数;最后通过整车性能校核确认变速器的适配性及优越性。结果表明为纯电动客车匹配的自动机械式变速器在能够满足整车动力性能的前提下对提高纯电动客车续驶里程具有积极意义。 相似文献
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付尧 《中国人民大学教育学刊》2014,(2):29-39
本文将我国财政预算内教育经费支出占国内生产总值(GDP)的比例作为衡量政府教育投入努力程度以及教育经费充足程度的指标,并将其看做是一时间序列,采用差分自回归移动平均模型(ARIMA)对中国1979年至2008年的财政预算内教育经费进行了分析和预测。结果显示,采用ARIMA(0,2,1)模型进行拟合,获得了较为理想的效果... 相似文献
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《赤峰学院学报(自然科学版)》2016,(12)
水位预报因具有数据直观,复核方便,容易确定避险区域等优点而逐渐取代复杂的流量预报.平稳时间序列模型计算简单,容易操作,是水位预报的理想方法.本文对比分析自回归模型、滑动平均模型和自回归滑动平均模型,以简捷原则为原则建立了自回归模型的参数计算方法和水位预报模型.并以丹东一水文站25年水位资料为例进行了两个阶数对比,结果表明3阶预报精度高于5阶,比选结果认为一般情况下可只进行3阶预报就能满足精度要求. 相似文献
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本文通过分析传染病的特性,建立了时序自回归差分方程模型,对SARS传染病的流行规律进行了进一步研究,并讨论了平衡点及其稳定性.仿真结果表明,使用自回归差分方程模型预测传染病的流行趋势,具有精度高、简单易行的特点. 相似文献
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针对现代生产工业过程中数据的非线性多模态特征,提出了一种基于人工大猩猩部队优化动态核主元分析(GTO-DKPCA)的故障监测方法。利用自回归移动平均时间序列模型和核主成分分析(KPCA)方法构建DKPCA模型,对过程各阶段的批次数据进行DKPCA处理。通过正常数据和故障数据特征构建自适应度函数,利用人工大猩猩部队优化算法对DKPCA核参数进行优化,以发现最优的非线性特征;通过计算各时间点的霍特林统计量T2和平方预测误差(SPE)统计量进行故障监测。青霉素发酵过程故障监测结果表明,GTO-DKPCA方法比多向核主元分析(MKPCA)和多动态核主元分析(BDKPCA)有更好的监测效果,适应性和准确性更高。 相似文献
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针对车载电池SOC难以精确预测的问题,提出以CPSO算法优化LSSVM模型参数,避免了参数选择的盲目性,提高了测量精度及泛化能力。利用ADVISOR软件采集车载电池各项性能参数,其中,电流、电压及温度数据作为CPSO-LSSVM预测模型的输入,SOC作为预测模型的输出。验证结果表明:CPSOLSSVM相比PSO-LSSVM预测模型预测最大绝对误差降低了3.06%,平均相关误差降低了0.35%,为车载电池SOC的预测提供一新方法。 相似文献
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在基于固定窗口宽度滑动窗口模型的基础上。提出了一种基于回归参数存储的预测模型,该模型设置了计算区、数据区和参数区。计算区用于获得最近一个滑动窗口中的数据。数据区用于接收新数据,参数区存储最近若干组滑动窗口数据所计算得到的线性回归参数值,作为计算预测结果的原始数据集。按照这种模型的处理思路。提出了一种基于数据平滑技术的回归预测算法,随着窗口的滑动。对数据区中的数据进行回归分析,获得前面若干组滑动窗口数据的回归函数并存入参数区中,检验当前窗口中数据获得的回归函数预测效果。实验分析表明。通过修正当前回归函数的参数。可以使预测函数的预测精度得到很大程度的提高。 相似文献
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SPSS预测模型在商场中的应用 总被引：3，自引：0，他引：3
徐林 《宁波职业技术学院学报》2005,9(2):44-47
探讨了SPSS 12统计软件包中回归、指数平滑及ARIMA(自回归求和移动平均)等时间序列分析模块的建模及预测方法。根据金星商场1997年～2005年，1～12月的销售历史资料，建立对数模型、指数平滑模型和ARIMA乘积模型，并对三的预测结果进行比较分析，给出了平均相对误差。得出ARIMA乘积模型误差最小，它适于对有趋势性和周期性的观察数据进行预测。SPSS12统计软件包时间序列分析模块操作方便，在商场统计预测中有广阔的应用前景。 相似文献
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荷电状态(SOC)是电动汽车动力电池的核心性能指标。为了进一步提高锂离子电池组单体电池荷电状态预测精度,提出一种基于改进PNGV模型的电池内阻辨识与SOC预测。根据锂离子动力电池的特性分析,建立改进型PNGV模型。利用实验采集的数据和最小二乘算法实现内阻的在线识别。通过该内阻辨识算法,更加准确地反映电池的当前电压。根据预测更加准确的电压,从而提出基于数据融合PHM法预测电池的SOC,该方法基于实验数据和灰色预测模型来估算电池的荷电状态。仿真和实验结果表明,基于内阻辨识的SOC预测更准确,具有较强的工程实用性。 相似文献
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《Structural equation modeling》2013,20(3):347-368
Several papers have been devoted to the use of structural equation modeling (SEM) software in fitting autoregressive moving average (ARMA) models to a univariate series observed in a single subject. Van Buuren (1997) went beyond specification and examined the nature of the estimates obtained with SEM software. Although the results were mixed, he concluded that these parameter estimates resemble true maximum likelihood (ML) estimates. Molenaar (1999) argued that the negative findings for pure moving average models might be due to the absence of invertibility constraints in Van Buuren's simulation experiment. The aim of this article is to (a) reexamine the nature of SEM estimates of ARMA parameters; (b) replicate Van Buuren's simulation experiment in light of Molenaar's comment; and (c) examine the behavior of the log-likelihood ratio test. We conclude that estimates of ARMA parameters obtained with SEM software are identical to those obtained by univariate stochastic model preliminary estimation, and are not true ML estimates. Still, these estimates, which may be viewed as moment estimates, have the same asymptotic properties as ML estimates for pure autoregressive (AR) processes. For pure moving average (MA) processes, they are biased and less efficient. The estimates from SEM software for mixed processes seem to have the same asymptotic properties as ML estimates. Furthermore, the log-likelihood ratio is reliable for pure AR processes, but this is not the case for pure MA processes. For mixed processes, the behavior of the log-likelihood ratio varies, and in this case these statistics should be handled with caution. 相似文献
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提出了一种以 FA RIMA( p,d,q)模型为基础 ,对 MPEG VBR业务在综合业务网络传输的带宽进行动态分配的新方法 .FARIMA( p,d,q)模型既可描述业务的长相关特性 ,又可描述业务的短相关特性 ,以其为基础的业务预测较准确 .文中对建模和预报方法进行了简化 ,并通过仿真进行了验证 .结果表明本方法可减少对缓冲区的需求 ,并减少了丢包率 相似文献
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S. Sp. Pappas L. Ekonomou V. C. Moussas P. Karampelas S. K. Katsikas 《浙江大学学报(A卷英文版)》2008,9(12):1724-1730
Designers are required to plan for future expansion and also to estimate the grid＇s future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm （MMPA）, for short-term electricity load forecasting using real data. The grid＇s utilization is initially modeled using a multiplicative seasonal ARIMA （autoregressive integrated moving average） model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA （autoregressive moving average） or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults （blackouts） are also modeled. If the load pattern does not match the normal behavior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model. 相似文献
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为了提高电动车铅酸蓄电池的电池荷电状态(SOC)预测精度,将粒子优化算法(PSO)引入到支持向量机(SVM)中,建立了PSO-SVM电动车铅酸蓄电池SOC预测模型,模型输入量为电池的电压和电流,输出量为SOC。采用PSO算法对SVM的惩罚因子C和径向基函数宽度σ寻优,降低了SVM参数取值的盲目性,提高了预测精度。设计了铅酸蓄电池数据智能采集系统,并进行了实际运行车辆电池数据采集。在advisor2002软件中获取的电池数据和实际车辆电池运行数据的基础上,进行了模型训练和预测。结果表明,PSO-SVM预测模型相对传统的BP、RBF和SVM预测模型具有更好的精度和推广能力,满足了"SOC估算精度小于5%"的要求,从而表明该模型是有效的、可行的,并具有较好的工程实用价值。 相似文献
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《Structural equation modeling》2013,20(2):215-231
The purpose of this study was to evaluate the robustness of estimated growth curve models when there is stationary autocorrelation among manifest variable errors. The results suggest that when, in practice, growth curve models are fitted to longitudinal data, alternative rival hypotheses to consider would include growth models that also specify autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes. AR (i.e., simplex) processes are commonly found in longitudinal data and may diminish the ability of a researcher to detect growth if not explicitly modeled. MA and ARMA processes do not affect the fit of growth models, but do notably bias some of the parameters. 相似文献
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动力电池的荷电状态(State of Charge,SOC)是预估电动汽车剩余有效行驶里程的重要参数之一。为提高锂电池SOC 的估算精度,考虑了温度对锂电池特性的影响。通过实验得到温度对电池容量的关系曲线,以及得到OCV-SOC-T 的函数映射关系,基于二阶RC 等效电路模型,利用带遗忘因子递推最小二乘法(Forgetting Factor Recursive Least Square, FFRLS)对模型进行实时在线参数辨识。在不同温度和工况条件下,采用扩展卡尔曼滤波(Extended Kalman filter,EKF)和无迹卡尔曼滤波( Un-scented Kalman filter, UKF)算法对锂电池的SOC 进行估算并对比验证,结果表明,EKF 在动态压力测试工况(DST)和美国联邦城市运行工况(FUDS) 的均方根误差分别在4.93%和4.69%以内,UKF 在DST 和FUDS 工况下的均方根误差分别在1.47%和1.49%以内。研究结果表明,FFRLS联合EKF和UKF都可以实时估算SOC,且在不同温度和不同工况条件下,UKF算法相较于EKF算法,抗干扰能力更强,估算精度更高,收敛性更好。 相似文献
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胡永仕杨悠悠张阳 《福建工程学院学报》2021,(3):230-235
为克服公交调度优化模型中纯电动公交车受续航里程约束、未考虑驾驶员舒适度的不足,提出了人-车固定模式的纯电动公交车柔性调度优化方法。采用休憩时长为衡量驾驶员舒适度的指标,将保证驾驶员舒适度产生的负面边际效应量化为延误成本,以公交企业总成本最小为目标构建优化调度模型,引入改进的粒子群算法求解。改进算法通过调整粒子群算法的位置和更新机制解决传统粒子群算法易陷入局部极值的问题,进一步提高算法精度。实验结果表明,柔性调度优化方法能有效降低公交企业的总运营成本,具有一定的实用性。 相似文献
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In this paper, an efficient model structure composed of a second-order resistance-capacitance network and a simply analytical
open circuit voltage versus state of charge (SOC) map is applied to characterize the voltage behavior of a lithium iron phosphate
battery for electric vehicles (EVs). As a result, the overpotentials of the battery can be depicted using a second-order circuit
network and the model parameterization can be realized under any battery loading profile, without a special characterization
experiment. In order to ensure good robustness, extended Kalman filtering is adopted to recursively implement the calibration
process. The linearization involved in the calibration algorithm is realized through recurrent derivatives in a recursive
form. Validation results show that the recursively calibrated battery model can accurately delineate the battery voltage behavior
under two different transient power operating conditions. A comparison with a first-order model indicates that the recursively
calibrated second-order model has a comparable accuracy in a major part of the battery SOC range and a better performance
when the SOC is relatively low. 相似文献