利用最小二乘回归支持向量机LS-SVMR(least square support vectors machines for regression)对2个不同长度的时间序列资料,国家气候中心1982年1月~2005年12月Nino3区逐月海温距平指数(短序列),及1950年1月~2006年12月Nino3区逐月海温...利用最小二乘回归支持向量机LS-SVMR(least square support vectors machines for regression)对2个不同长度的时间序列资料,国家气候中心1982年1月~2005年12月Nino3区逐月海温距平指数(短序列),及1950年1月~2006年12月Nino3区逐月海温距平指数(长序列)资料进行了预测试验,以验证支持向量机对气候变化中非线性时间序列的预测效果。结果表明:通过训练建立的最小二乘回归支持向量机模型,较好地反映了Nino3区海温距平指数的变化规律,36个月的预报效果较好,具有一定的可信度。资料的长度越长,预测结果与实测值的变化趋势越接近,但资料长度对均方根预报误差不敏感。展开更多
传统的分数年龄假设(fractional age assumption,FAA)形式简单且计算容易,但它们却存在死力函数在整数年龄处有较大跳跃的缺点,并且无法保证能精确地捕捉到生存函数的真实趋势。最小二乘支持向量回归机(least square support vector reg...传统的分数年龄假设(fractional age assumption,FAA)形式简单且计算容易,但它们却存在死力函数在整数年龄处有较大跳跃的缺点,并且无法保证能精确地捕捉到生存函数的真实趋势。最小二乘支持向量回归机(least square support vector regression,LSSVR)作为机器学习领域的一项经典技术被广泛应用于对统计数据的回归与分析中。从机器学习的新视角来研究寿险精算理论中的生命表数据,对生存函数数据进行回归,并用成功拟合的生存函数构建死力函数及平均余命函数。LSSVR模型对生存函数拟合的有效性通过Makeham函数来进行验证,并与经典的三个FAA模型进行比较,实验表明,LSSVR模型的回归能力远高于经典的FAA模型。展开更多
In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support ve...In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.展开更多
This article adopts three artificial intelligence techniques, Gaussian Process Regression（GPR）, Least Square Support Vector Machine（LSSVM） and Extreme Learning Machine（ELM）, for prediction of rock depth（d） at ...This article adopts three artificial intelligence techniques, Gaussian Process Regression（GPR）, Least Square Support Vector Machine（LSSVM） and Extreme Learning Machine（ELM）, for prediction of rock depth（d） at any point in Chennai. GPR, ELM and LSSVM have been used as regression techniques.Latitude and longitude are also adopted as inputs of the GPR, ELM and LSSVM models. The performance of the ELM, GPR and LSSVM models has been compared. The developed ELM, GPR and LSSVM models produce spatial variability of rock depth and offer robust models for the prediction of rock depth.展开更多
文摘利用最小二乘回归支持向量机LS-SVMR(least square support vectors machines for regression)对2个不同长度的时间序列资料,国家气候中心1982年1月~2005年12月Nino3区逐月海温距平指数(短序列),及1950年1月~2006年12月Nino3区逐月海温距平指数(长序列)资料进行了预测试验,以验证支持向量机对气候变化中非线性时间序列的预测效果。结果表明:通过训练建立的最小二乘回归支持向量机模型,较好地反映了Nino3区海温距平指数的变化规律,36个月的预报效果较好,具有一定的可信度。资料的长度越长,预测结果与实测值的变化趋势越接近,但资料长度对均方根预报误差不敏感。
文摘传统的分数年龄假设(fractional age assumption,FAA)形式简单且计算容易,但它们却存在死力函数在整数年龄处有较大跳跃的缺点,并且无法保证能精确地捕捉到生存函数的真实趋势。最小二乘支持向量回归机(least square support vector regression,LSSVR)作为机器学习领域的一项经典技术被广泛应用于对统计数据的回归与分析中。从机器学习的新视角来研究寿险精算理论中的生命表数据,对生存函数数据进行回归,并用成功拟合的生存函数构建死力函数及平均余命函数。LSSVR模型对生存函数拟合的有效性通过Makeham函数来进行验证,并与经典的三个FAA模型进行比较,实验表明,LSSVR模型的回归能力远高于经典的FAA模型。
基金co-supported by Aeronautical Science Foundation of China (No. 2010ZB52011)Funding of Jiangsu Innovation Program for Graduate Education (No.CXLX11_0213)
文摘In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.
文摘This article adopts three artificial intelligence techniques, Gaussian Process Regression（GPR）, Least Square Support Vector Machine（LSSVM） and Extreme Learning Machine（ELM）, for prediction of rock depth（d） at any point in Chennai. GPR, ELM and LSSVM have been used as regression techniques.Latitude and longitude are also adopted as inputs of the GPR, ELM and LSSVM models. The performance of the ELM, GPR and LSSVM models has been compared. The developed ELM, GPR and LSSVM models produce spatial variability of rock depth and offer robust models for the prediction of rock depth.
