首页|New Findings on Support Vector Machines from Jiangsu University Summarized (Displacement Self-sensing Control of Permanent Magnet Assisted Bearingless Synchronous Reluctance Motor Based On Least Square Support Vector Machine Optimized By…)
New Findings on Support Vector Machines from Jiangsu University Summarized (Displacement Self-sensing Control of Permanent Magnet Assisted Bearingless Synchronous Reluctance Motor Based On Least Square Support Vector Machine Optimized By…)
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Researchers detail new data in Support Vector Machines. According to news reporting originating from Zhenjiang, People's Republic of China, by NewsRx correspondents, research stated, “In order to solve the problems of low integration, low reliability, and high cost caused by mechanical sensors used in bearingless synchronous reluctance motor (PMa-BSynRM) control system, a novel displacement self-sensing control method using a least square support vector machine (LSSVM) left inverse system is proposed. First, the working principle of the PMa-BSynRM is introduced and the mathematical model of the PMa-BSynRM is derived.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Jiangsu University, “Second, the observation principle of the left-inverse system of the PMa-BSynRM is explained and the left-invertibility of the displacement subsystem is proved. Thirdly, the improved NSGA-II algorithm is utilized to optimize the regularization parameter and the bandwidth of LSSVM, and the displacement self-sensing control system is constructed. The simulations of speed variation and anti-interference are performed, which proves the dynamic tracking performance of the displacement. Finally, the static suspension, speed variation and anti-interference experiments are carried out.”
ZhenjiangPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningSupport Vector MachinesVector MachinesJiangsu University
NETL
NSTL
IEEE
