An Improved Force Characteristic Curve Fitting Algorithm of Urban Rail Vehicles. (1st April 2022)

• Record Type: Journal Article
• Authors: Wang, Longda; Wang, Xingcheng; Liu, Gang
• Other Names: Pau Giovanni Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-04-01
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/9910982
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : In this paper, an improved force characteristic curve fitting memetic algorithm of urban rail vehicles is proposed for establishing precise train operation models. In order to improve the memetic algorithm global convergence, three strategies are adopted. In the improved memetic algorithm framework, an improved moth-flame optimization is used in global search; an improved simulated annealing is applied in local search; a new learning mechanism incorporated into reverse learning is adopted. Experimental simulation results under real-time data monitoring system show that the improved memetic algorithm proposed in this paper can increase the optimization performance effectively so more perfect force characteristic curve fitting effort can be obtained, and the calculated average force error and max running distance error can be reduced effectively. Moreover, the above relative results indicate that the train energy consumption model using the improved force characteristic curve fitting algorithm can obtain more precise energy consumption. Obviously, the improved force characteristic curve fitting algorithm can effectively improve the curve fitting precision.