AOSVR(Accurate Online Support Vector Regression)具有在线学习和模型在线更新的优点,可应用于交通流量的实时预测,其中算法的核函数的选择对模型的学习、推广和泛化能力起着重要的作用,但是至今有关核函数的选择缺乏科学的理论依据...AOSVR(Accurate Online Support Vector Regression)具有在线学习和模型在线更新的优点,可应用于交通流量的实时预测,其中算法的核函数的选择对模型的学习、推广和泛化能力起着重要的作用,但是至今有关核函数的选择缺乏科学的理论依据。为了进一步提高模型的学习和推广能力等,提出一种WT-AOSVR(Weight Table And Accurate Online Support Vector Regression)模型。对交通流进行数据挖掘,分类处理,构造支路AOSVR模型和权值表,在交通流预测时,通过搜索权值表就可以得到多条支路模型的一种加权组合模型。仿真实验表明该方法既提高了模型学习精度又保证了模型的泛化和推广能力,具有一定的应用价值。展开更多
Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It ...Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, mis paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market展开更多
Accurate fault prediction can obviously reduce cost and decrease the probability of accidents so as to improve the performance of the system testing and maintenance.Traditional fault prediction methods are always offl...Accurate fault prediction can obviously reduce cost and decrease the probability of accidents so as to improve the performance of the system testing and maintenance.Traditional fault prediction methods are always offline that are not suitable for online and real-time processing.For the complicated nonlinear and non-stationary time series,it is hard to achieve exact predicting result with single models such as support vector regression(SVR),artifieial neural network(ANN),and autoregressive moving average(ARMA).Combined with the accurate online support vector regression(AOSVR)algorithm and ARMA model,a new online approach is presented to forecast fault with time series prediction.The fault trend feature can be extracted by the AOSVR with global kernel for general fault modes.Moreover,its prediction residual that represents the local high-frequency components is synchronously revised and compensated by the sliding time window ARMA model.Fault prediction with combined AOSVR and ARMA can be realized better than with the single one.Experiments on Tennessee Eastman process fault data show the new method is practical and effective.展开更多
文摘AOSVR(Accurate Online Support Vector Regression)具有在线学习和模型在线更新的优点,可应用于交通流量的实时预测,其中算法的核函数的选择对模型的学习、推广和泛化能力起着重要的作用,但是至今有关核函数的选择缺乏科学的理论依据。为了进一步提高模型的学习和推广能力等,提出一种WT-AOSVR(Weight Table And Accurate Online Support Vector Regression)模型。对交通流进行数据挖掘,分类处理,构造支路AOSVR模型和权值表,在交通流预测时,通过搜索权值表就可以得到多条支路模型的一种加权组合模型。仿真实验表明该方法既提高了模型学习精度又保证了模型的泛化和推广能力,具有一定的应用价值。
基金This paper is about a project financed by the National Outstanding Young Investigator Grant (6970025)863 High Tech Development Plan of China (2001AA413910) the Project of National Natural Science Foundation (60274054) the Key Project of National Natural Science Foundation (59937150)it is also supported by its cooperating project financed by 863 High Tech Development Plan of China (2004AA412050).
文摘Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, mis paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market
文摘Accurate fault prediction can obviously reduce cost and decrease the probability of accidents so as to improve the performance of the system testing and maintenance.Traditional fault prediction methods are always offline that are not suitable for online and real-time processing.For the complicated nonlinear and non-stationary time series,it is hard to achieve exact predicting result with single models such as support vector regression(SVR),artifieial neural network(ANN),and autoregressive moving average(ARMA).Combined with the accurate online support vector regression(AOSVR)algorithm and ARMA model,a new online approach is presented to forecast fault with time series prediction.The fault trend feature can be extracted by the AOSVR with global kernel for general fault modes.Moreover,its prediction residual that represents the local high-frequency components is synchronously revised and compensated by the sliding time window ARMA model.Fault prediction with combined AOSVR and ARMA can be realized better than with the single one.Experiments on Tennessee Eastman process fault data show the new method is practical and effective.
