| 英文标题 | BN parameter learning algorithm based on dynamic weighted transfer learning |
| 作者英文名 | Guo Wenqiang, Xu Cheng, Xiao Qinkun, Li Mengran |
| 机构英文名 | 1.a.School of Electronic Information & Artificial Intelligence,b.School of Electrical & Control Engineering,Shaanxi University of Science & Technology,Xi'an 710021,China;2.School of Electronic Information Engineering,Xi'an University of Technology,Xi'an 710021,China |
| 英文摘要 | To solve the problem of Bayesian network(BN) parameter estimation accuracy under small dataset conditions, this paper proposed a parameter dynamic weighted transfer learning algorithm(DWTL) based on varying weight transfer learning. Firstly, the presented algorithm used MAP and the MLE method to learn the initial parameters of the target domain and the parameters of each source domain. Then, it obtained the source weight factors of the source domain by the different data source contributions. Based on the sample statistic, this method helped to obtain the final target parameters by fusing the data size threshold values, the balance coefficients for the target initial parameters with the source domain parameters. The experimental results show that under the condition of the small data set, the learning accuracy of DWTL algorithm is better than MLE algorithm, MAP algorithm and traditional transfer learning algorithm(LP). Under the condition of sufficient data set, the learning accuracy of DWTL algorithm approaches the classical MLE algorithm, and verifies the correctness of the algorithm. Moreover, it demonstrates successful application to real-world bearing fault diagnosis case studies. Comparing with the LP algorithm, the DWTL algorithm achieves about 10% enhancement for the average diagnosis precision. |
| 英文关键词 | small dataset; Bayesian network; transfer learning; parameter learning |