Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coeff...Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fited by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.展开更多
射频识别技术(radio frequency identification,RFID)以其非接触、非视距、低成本及高精度等优点成为室内定位技术的研究热点.为了加强信号稳定性并提高实时性,该文用流水线方式接收到的包信息作为定位信号参数,针对室内环境对信号传播...射频识别技术(radio frequency identification,RFID)以其非接触、非视距、低成本及高精度等优点成为室内定位技术的研究热点.为了加强信号稳定性并提高实时性,该文用流水线方式接收到的包信息作为定位信号参数,针对室内环境对信号传播影响的复杂性,提出了流水线型局部加权回归定位算法,将室内环境对信号传播到各位置的影响融合进算法,以实现精确定位.实验表明,对于室内定位,所提出的基于RFID技术的流水线型局部加权回归定位算法相对于经典的LANDMARK算法和VIRE算法,定位精度分别提高56.56%和36.73%.在多目标的情况下,也可以实现实时精确的定位跟踪,具有良好的实用价值和应用前景.展开更多
To overcome the limitations of the traditional stage-discharge models in describing the dynamic characteristics of a river, a machine learning method of non-parametric regression, the locally weighted regression metho...To overcome the limitations of the traditional stage-discharge models in describing the dynamic characteristics of a river, a machine learning method of non-parametric regression, the locally weighted regression method was used to estimate discharge. With the purpose of improving the precision and efficiency of river discharge estimation, a novel machine learning method is proposed： the clustering-tree weighted regression method. First, the training instances are clustered. Second, the k-nearest neighbor method is used to cluster new stage samples into the best-fit cluster. Finally, the daily discharge is estimated. In the estimation process, the interference of irrelevant information can be avoided, so that the precision and efficiency of daily discharge estimation are improved. Observed data from the Luding Hydrological Station were used for testing. The simulation results demonstrate that the precision of this method is high. This provides a new effective method for discharge estimation.展开更多
基金the National Natural Science Foundation of China (No.60075001) and Xi'anJiaotong University Natural Science Foundation.
文摘Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fited by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.
文摘射频识别技术(radio frequency identification,RFID)以其非接触、非视距、低成本及高精度等优点成为室内定位技术的研究热点.为了加强信号稳定性并提高实时性,该文用流水线方式接收到的包信息作为定位信号参数,针对室内环境对信号传播影响的复杂性,提出了流水线型局部加权回归定位算法,将室内环境对信号传播到各位置的影响融合进算法,以实现精确定位.实验表明,对于室内定位,所提出的基于RFID技术的流水线型局部加权回归定位算法相对于经典的LANDMARK算法和VIRE算法,定位精度分别提高56.56%和36.73%.在多目标的情况下,也可以实现实时精确的定位跟踪,具有良好的实用价值和应用前景.
基金supported by the Key Fund Project of the Sichuan Provincial Department of Education (Grant No. 11ZA009)the Fund Project of Sichuan Provincial Key Laboratory of Fluid Machinery (Grant No.SBZDPY-11-5)the Key Scientific Research Project of Xihua University (Grant No. Z1120413)
文摘To overcome the limitations of the traditional stage-discharge models in describing the dynamic characteristics of a river, a machine learning method of non-parametric regression, the locally weighted regression method was used to estimate discharge. With the purpose of improving the precision and efficiency of river discharge estimation, a novel machine learning method is proposed： the clustering-tree weighted regression method. First, the training instances are clustered. Second, the k-nearest neighbor method is used to cluster new stage samples into the best-fit cluster. Finally, the daily discharge is estimated. In the estimation process, the interference of irrelevant information can be avoided, so that the precision and efficiency of daily discharge estimation are improved. Observed data from the Luding Hydrological Station were used for testing. The simulation results demonstrate that the precision of this method is high. This provides a new effective method for discharge estimation.
