摘要
针对相关向量机在预测大样本故障数据中存在学习效率低和过度学习的不足,将粒计算和相关向量机理论系统结合,提出一种基于粒度相关向量机的故障预测方法。首先,以模糊C均值聚类作为粒化方法,将原数据集划分成一系列信息粒并提取其粒心以代替该粒中的样本;然后,将粒心的集合作为训练集对相关向量机的模型进行训练;最后,以训练得到的模型对未知数据进行预测。仿真结果表明,聚类数目和核参数的合理选取是保证粒度相关向量机性能的关键,粒度相关向量机在显著提高对大样本数据学习效率的同时,避免了过度学习,具有较高的预测精度。
Aiming at the shortages of low learning efficiency and overfitting of relevance vector machine （ RVM ） in the prediction of large-scale fault data with RVM, the theories of granular computing （GrC） and RVM were combined systemly, then a fault prediction method of granular relevance vector machine （GRVM） was proposed. Firstly, taking fuzzy C-means clustering （FCM） as the granulation method, then by FCM, the original data set was granulated into several granules and replaced by the clustering centers. Secondly, the clustering centers set was taken as the training set to train the model of RVM. Finally, we used the trained model to predict the unseen data. The simulation results indicate that, the suitable selection of clustering number and kernel pa- rameter is the key to ensure the performance of GRVM, and GRVM can improve the learning efficiency while it avoids the overfit- ting and keeps the high prediction accuracy.
出处
《计算机与现代化》
2016年第9期91-95,99,共6页
Computer and Modernization
