This paper mainly deals with the Bayesian statistical inference theory on the VAR(p) forecasting model based on the parameters’ Minnesota conjugate prior distribution,including the prior distribution’s structure, th...This paper mainly deals with the Bayesian statistical inference theory on the VAR(p) forecasting model based on the parameters’ Minnesota conjugate prior distribution,including the prior distribution’s structure, the parameters’ posterior distribution, and compares the forecasting accuracy of AR,VAR and BVAR model.展开更多
为实现环境激励下复杂钢结构的损伤预警,提出一种基于粒子群优化(particle swarm optimization,简称PSO)的支持向量回归(support vector regression,简称SVR)-时间序列(auto-regressive and moving average model,简称ARMA)组合模型用...为实现环境激励下复杂钢结构的损伤预警,提出一种基于粒子群优化(particle swarm optimization,简称PSO)的支持向量回归(support vector regression,简称SVR)-时间序列(auto-regressive and moving average model,简称ARMA)组合模型用于频率预测,并结合均值控制图法将其用于复杂钢结构的损伤预警中。所提出频率预测模型的准确性和有效性采用潍坊市白浪河摩天轮钢结构实测数据进行验证。验证结果表明:与基本SVR模型、SVR-ARMA模型和PSO-SVR模型相比,所提模型具有更高的泛化能力和预测精度;在白浪河摩天轮钢结构的损伤预警中,基于粒子群优化的SVR-ARMA组合模型可检出由损伤造成模态频率轻微的异常变化,具有较强的损伤敏感性。研究成果可为环境激励下复杂钢结构的损伤预警提供参考。展开更多
提出一种基于非线性自回归时间序列模型(gereral expression for linear and nonlinear auto-regressive mod-el,简称GNAR模型)的机械系统状态识别与故障诊断方法。利用采集系统工作过程中的特征信号建立GNAR模型;用主成分分析策略生成...提出一种基于非线性自回归时间序列模型(gereral expression for linear and nonlinear auto-regressive mod-el,简称GNAR模型)的机械系统状态识别与故障诊断方法。利用采集系统工作过程中的特征信号建立GNAR模型;用主成分分析策略生成模型特征量,对训练样本的特征量进行识别和分类,得到各种参考模式;将几何距离判别函数作为状态分类的原则,根据待判系统特征量与各类参考模式的Euclide距离进行状态识别和故障判别。对车床颤振试验数据及高速离心空气压缩机故障数据的分析表明,该方法快捷、高效,诊断成功率较好,具有良好的工程应用前景。展开更多
Objective To construct a model of Seasonal Autoregressive Integrated Moving Average （SARIMA） for forecasting the epidemic of Japanese encephalitis （JE） in Xianyang, Shaanxi, China, and provide valuable reference ...Objective To construct a model of Seasonal Autoregressive Integrated Moving Average （SARIMA） for forecasting the epidemic of Japanese encephalitis （JE） in Xianyang, Shaanxi, China, and provide valuable reference information for JE control and prevention. Methods Theoretically epidemiologic study was employed in the research process. Monthly incidence data on JE for the period from Jan 2005 to Sep 2014 were obtained from a passive surveillance system at the Center for Diseases Prevention and Control in Xianyang, Shaanxi province. An optimal SARIMA model was developed for JE incidence from 2005 to 2013 with the Box and Jenkins approach. This SARIMA model could predict JE incidence for the year 2014 and 2015. Results SARIMA （1, 1, 1） （2, 1, 1）12 was considered to be the best model with the lowest Bayesian information criterion, Akaike information criterion, Mean Absolute Error values, the highest R2, and a lower Mean Absolute Percent Error. SARIMA （1, 1, 1） （2, 1, 1）12 was stationary and accurate for predicting JE incidence in Xianyang. The predicted incidence, around 0.3/100 000 from June to August in 2014 with low errors, was higher compared with the actual incidence. Therefore, SARIMA （1, 1, 1） （2, 1, 1）12 appeared to be reliable and accurate and could be applied to incidence prediction. Conclusions The proposed prediction model could provide clues to early identification of the JE incidence that is increased abnormally （≥0.4/100 000）. According to the predicted results in 2014, the JE incidence in Xianyang will decline slightly and reach its peak from June to August.The authors wish to thank the staff from the CDCs from 13 counties of Xianyang, Shaanxi province, China, for their contribution to Japanese encephalitis cases reporting.展开更多
文摘This paper mainly deals with the Bayesian statistical inference theory on the VAR(p) forecasting model based on the parameters’ Minnesota conjugate prior distribution,including the prior distribution’s structure, the parameters’ posterior distribution, and compares the forecasting accuracy of AR,VAR and BVAR model.
文摘为实现环境激励下复杂钢结构的损伤预警,提出一种基于粒子群优化(particle swarm optimization,简称PSO)的支持向量回归(support vector regression,简称SVR)-时间序列(auto-regressive and moving average model,简称ARMA)组合模型用于频率预测,并结合均值控制图法将其用于复杂钢结构的损伤预警中。所提出频率预测模型的准确性和有效性采用潍坊市白浪河摩天轮钢结构实测数据进行验证。验证结果表明:与基本SVR模型、SVR-ARMA模型和PSO-SVR模型相比,所提模型具有更高的泛化能力和预测精度;在白浪河摩天轮钢结构的损伤预警中,基于粒子群优化的SVR-ARMA组合模型可检出由损伤造成模态频率轻微的异常变化,具有较强的损伤敏感性。研究成果可为环境激励下复杂钢结构的损伤预警提供参考。
文摘提出一种基于非线性自回归时间序列模型(gereral expression for linear and nonlinear auto-regressive mod-el,简称GNAR模型)的机械系统状态识别与故障诊断方法。利用采集系统工作过程中的特征信号建立GNAR模型;用主成分分析策略生成模型特征量,对训练样本的特征量进行识别和分类,得到各种参考模式;将几何距离判别函数作为状态分类的原则,根据待判系统特征量与各类参考模式的Euclide距离进行状态识别和故障判别。对车床颤振试验数据及高速离心空气压缩机故障数据的分析表明,该方法快捷、高效,诊断成功率较好,具有良好的工程应用前景。
基金Supported by the Youth Project of Shaanxi University of Chinese Medicine(2015QN05)
文摘Objective To construct a model of Seasonal Autoregressive Integrated Moving Average （SARIMA） for forecasting the epidemic of Japanese encephalitis （JE） in Xianyang, Shaanxi, China, and provide valuable reference information for JE control and prevention. Methods Theoretically epidemiologic study was employed in the research process. Monthly incidence data on JE for the period from Jan 2005 to Sep 2014 were obtained from a passive surveillance system at the Center for Diseases Prevention and Control in Xianyang, Shaanxi province. An optimal SARIMA model was developed for JE incidence from 2005 to 2013 with the Box and Jenkins approach. This SARIMA model could predict JE incidence for the year 2014 and 2015. Results SARIMA （1, 1, 1） （2, 1, 1）12 was considered to be the best model with the lowest Bayesian information criterion, Akaike information criterion, Mean Absolute Error values, the highest R2, and a lower Mean Absolute Percent Error. SARIMA （1, 1, 1） （2, 1, 1）12 was stationary and accurate for predicting JE incidence in Xianyang. The predicted incidence, around 0.3/100 000 from June to August in 2014 with low errors, was higher compared with the actual incidence. Therefore, SARIMA （1, 1, 1） （2, 1, 1）12 appeared to be reliable and accurate and could be applied to incidence prediction. Conclusions The proposed prediction model could provide clues to early identification of the JE incidence that is increased abnormally （≥0.4/100 000）. According to the predicted results in 2014, the JE incidence in Xianyang will decline slightly and reach its peak from June to August.The authors wish to thank the staff from the CDCs from 13 counties of Xianyang, Shaanxi province, China, for their contribution to Japanese encephalitis cases reporting.
