首页|Research Data from Shanghai Maritime University Update Understanding of Support Vector Machines (Rolling bearing fault diagnosis based on fine-grained multi-sca le Kolmogorov entropy and WOA-MSVM)
Research Data from Shanghai Maritime University Update Understanding of Support Vector Machines (Rolling bearing fault diagnosis based on fine-grained multi-sca le Kolmogorov entropy and WOA-MSVM)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in . Accor ding to news originating from Shanghai, People's Republic of China, by NewsRx ed itors, the research stated, “In allusion to solve the issue of fault diagnosis f or bearing and other rotatory machinery, a technique based on fined-grained mult i-scale Kolmogorov entropy and whale optimized multi-class support vector machin e (abbreviated as FGMKE-WOA-MSVM) is proposed.”
NETL
NSTL
