海军航空工程学院学报Issue(2)：154-160,7.
相关向量机及其在故障诊断与预测中的应用
Relevance Vector Machine and Its Applications in Fault Diagnosis and Prognosis
马登武 1范庚 1张继军1
作者信息
- 1. 海军航空工程学院兵器科学与技术系，山东烟台264001
- 折叠
摘要
Abstract
Relevance vector machine (RVM) is a new machine learning method based on sparse Bayesian learn⁃ing theory, which has probabilistic outputs, high sparsity, simple parameter tuning and flexible selection of ker⁃nel function. RVM has overcome many inherent defects of typical machine learning methods, such as ANN and SVM. The research progress of relevance vector machine (RVM) was summarized in model selection and optimi⁃zation, model computational efficiency and model robustness improvement. The research status of applications of RVM in fault diagnosis and prognosis was introduced. The existing problems in the current research were ana⁃lyzed and the development trends of fault diagnosis and prognosis based on RVM were discussed.关键词
故障诊断/故障预测/相关向量机/机器学习Key words
fault diagnosis/fault prognosis/relevance vector machine/machine learning分类
计算机与自动化引用本文复制引用
马登武,范庚,张继军..相关向量机及其在故障诊断与预测中的应用[J].海军航空工程学院学报,2013,(2):154-160,7.
