阐述了装备可用度预测的重要性,并以此为需求牵引,构建了具有非线性、非平稳的装备可用度时间序列。基于奇异值分解滤波算法将其分解为尽量平滑的趋势成分和平稳的随机成分,分别应用粒子群训练的径向基神经网络和函数系数自回归模型进...阐述了装备可用度预测的重要性,并以此为需求牵引,构建了具有非线性、非平稳的装备可用度时间序列。基于奇异值分解滤波算法将其分解为尽量平滑的趋势成分和平稳的随机成分,分别应用粒子群训练的径向基神经网络和函数系数自回归模型进行预测,充分体现了2类模型各自的优势,使其相互补充,并进行实例分析,验证了模型和算法的有效性。实验与应用结果表明,该组合方法的预测性能和效果比单一使用RBF(radial basis function neural network)和FAR(functional-coefficient auto regressive model)进行预测更好,为装备可用度研究提供了一条新的思路。展开更多
One important model in handling the multivariate data is the varying-coefficient partially linear regression model. In this paper, the generalized likelihood ratio test is developed to test whether its coefficient fun...One important model in handling the multivariate data is the varying-coefficient partially linear regression model. In this paper, the generalized likelihood ratio test is developed to test whether its coefficient functions are varying or not. It is showed that the normalized proposed test follows asymptotically x2-distribution and the Wilks phenomenon under the null hypothesis, and its asymptotic power achieves the optimal rate of the convergence for the nonparametric hypotheses testing. Some simulation studies illustrate that the test works well.展开更多
文摘阐述了装备可用度预测的重要性,并以此为需求牵引,构建了具有非线性、非平稳的装备可用度时间序列。基于奇异值分解滤波算法将其分解为尽量平滑的趋势成分和平稳的随机成分,分别应用粒子群训练的径向基神经网络和函数系数自回归模型进行预测,充分体现了2类模型各自的优势,使其相互补充,并进行实例分析,验证了模型和算法的有效性。实验与应用结果表明,该组合方法的预测性能和效果比单一使用RBF(radial basis function neural network)和FAR(functional-coefficient auto regressive model)进行预测更好,为装备可用度研究提供了一条新的思路。
基金supported by National Natural Science Foundation of China under Grant No.1117112the Fund of Shanxi Datong University under Grant No.2010K4+1 种基金the Doctoral Fund of Ministry of Education of China under Grant No.20090076110001National Statistical Science Research Major Program of China under Grant No.2011LZ051
文摘One important model in handling the multivariate data is the varying-coefficient partially linear regression model. In this paper, the generalized likelihood ratio test is developed to test whether its coefficient functions are varying or not. It is showed that the normalized proposed test follows asymptotically x2-distribution and the Wilks phenomenon under the null hypothesis, and its asymptotic power achieves the optimal rate of the convergence for the nonparametric hypotheses testing. Some simulation studies illustrate that the test works well.
