Improved self-learning Particle Swarm Algorithm for Calibrating a Three-Axis Measuring System
This paper introduces self-learning mechanism into the basic particle swarm algorithm to present the improved particle swarm algorithm.The self-learning mechanism ensures that the particles can change their speed and positions base on self learning and the overall experience.The novel algorithm can dynamically increase the diversity of particle swarm offspring and reduce the human intervention in evolutionary process.Then,use the improved particle swarm algorithm to calibrate the errors of three-axis measuring system.Simulation results show that the improved particle swarm algorithm is effective and feasible and has a good performance in calibrating the errors of three-axis measuring system.
component Self learning mechanism PSO Threeaxis system Error correction
Qiang Wen Cheng-lin Mao Jia-song Wang Cui-cui Li Xiong Yang
Harbin Engineering University, College of Science Harbin, Heilongjiang Province Guizhou Aerospace Electronics Technology Co., Ltd. Guiyang, Guizhou Province
国际会议
秦皇岛
英文
1352-1357
2015-09-18（万方平台首次上网日期，不代表论文的发表时间）