An Improved Force Characteristic Curve Fitting Algorithm of Urban Rail Vehicles
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,...
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| Veröffentlicht in: | Journal of sensors 2022-04, Vol.2022, p.1-13 |
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| container_title | Journal of sensors |
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| creator | Wang, Longda Wang, Xingcheng Liu, Gang |
| description | 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. |
| doi_str_mv | 10.1155/2022/9910982 |
| format | Article |
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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.</description><identifier>ISSN: 1687-725X</identifier><identifier>EISSN: 1687-7268</identifier><identifier>DOI: 10.1155/2022/9910982</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Accuracy ; Algorithms ; Butterflies & moths ; Curve fitting ; Decomposition ; Design ; Energy consumption ; Error reduction ; Genetic algorithms ; Learning ; Mathematical models ; Optimization ; Optimization algorithms ; Parameter estimation ; Parameter identification ; Power ; Simulated annealing ; Simulation ; Traveling salesman problem ; Urban rail ; Vehicles ; Velocity</subject><ispartof>Journal of sensors, 2022-04, Vol.2022, p.1-13</ispartof><rights>Copyright © 2022 Longda Wang et al.</rights><rights>Copyright © 2022 Longda Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c404t-15f0501c13332e102bce1b5e50d58a770f02c02efce00a94ec58231366826df03</citedby><cites>FETCH-LOGICAL-c404t-15f0501c13332e102bce1b5e50d58a770f02c02efce00a94ec58231366826df03</cites><orcidid>0000-0002-7226-5900 ; 0000-0002-6239-7889 ; 0000-0003-4030-8125</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><contributor>Pau, Giovanni</contributor><contributor>Giovanni Pau</contributor><creatorcontrib>Wang, Longda</creatorcontrib><creatorcontrib>Wang, Xingcheng</creatorcontrib><creatorcontrib>Liu, Gang</creatorcontrib><title>An Improved Force Characteristic Curve Fitting Algorithm of Urban Rail Vehicles</title><title>Journal of sensors</title><description>In this paper, an improved force characteristic curve fitting memetic algorithm of urban rail vehicles is proposed for establishing precise train operation models. 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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. 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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley Online Library Open Access; Alma/SFX Local Collection |
| subjects | Accuracy Algorithms Butterflies & moths Curve fitting Decomposition Design Energy consumption Error reduction Genetic algorithms Learning Mathematical models Optimization Optimization algorithms Parameter estimation Parameter identification Power Simulated annealing Simulation Traveling salesman problem Urban rail Vehicles Velocity |
| title | An Improved Force Characteristic Curve Fitting Algorithm of Urban Rail Vehicles |
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