Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
