The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support v...The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau’s as the input data. Then coal reserves within a particular region were calculated. These calculated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.展开更多
Aiming at solving the blind estimation problem of dispreading spectrum sequence under low SNR, a spread-spectrum estimation algorithm based subspace tracking is studied in this paper. This method avoids the direct eig...Aiming at solving the blind estimation problem of dispreading spectrum sequence under low SNR, a spread-spectrum estimation algorithm based subspace tracking is studied in this paper. This method avoids the direct eigen decomposition, using the sliding window technique to obtain the code synchronization, then use segmentation subspace tracking method estimate spreading sequence and splice in a certain order to achieve pseudo-code blind estimation. The results show that the algorithm can complete the accurate estimation of PN code sequence in low SNR conditions, reduce the amount of data storage and be easy hardware implementation展开更多
基金Project 072400430420 supported by the Natural Science Foundation of Henan Province
文摘The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau’s as the input data. Then coal reserves within a particular region were calculated. These calculated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.
文摘Aiming at solving the blind estimation problem of dispreading spectrum sequence under low SNR, a spread-spectrum estimation algorithm based subspace tracking is studied in this paper. This method avoids the direct eigen decomposition, using the sliding window technique to obtain the code synchronization, then use segmentation subspace tracking method estimate spreading sequence and splice in a certain order to achieve pseudo-code blind estimation. The results show that the algorithm can complete the accurate estimation of PN code sequence in low SNR conditions, reduce the amount of data storage and be easy hardware implementation
