| 引用本文: | 蔡国强,邢宗义,潘丽莎,程晓卿,秦勇.采用遗传神经网络的轮轨力建模方法[J].哈尔滨工业大学学报,2012,44(7):114.DOI:10.11918/j.issn.0367-6234.2012.07.022 |
| CAI Guo-qiang,XING Zong-yi,PAN Li-sha,CHENG Xiao-qing,Qin yong.Modelling of wheel-rail force based on genetic neural networks[J].Journal of Harbin Institute of Technology,2012,44(7):114.DOI:10.11918/j.issn.0367-6234.2012.07.022 |
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| 摘要: |
| 为解决轮轨力建模问题,提出了一种基于遗传算法和径向基函数神经网络的轮轨力建模方法,该方法基于轨道不平顺输入实现了轮轨力的预测．在径向基函数神经网络的中心、宽度和权值参数上,分别采用遗传算法、最大距离法和最小二乘法来确定,从而提高建模精度并减轻该算法的计算量,实现了快速准确的轮轨力神经网络建模．仿真试验结果表明:提出的轮轨力建模方法具有较高的预测性能． |
| 关键词: 轨道不平顺 轮轨力 建模 神经网络 遗传算法 |
| DOI：10.11918/j.issn.0367-6234.2012.07.022 |
| 分类号:U216; TP183 |
| 基金项目:国家自然科学基金资助项目 (61074151) ；国家科技支撑计划资助项目(2011BAG01B05)；轨道交通国家重点实验室开放课题资助项目(RCS2009K010)；南京理工大学紫金之星资助项目 (2010GJPY007). |
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| Modelling of wheel-rail force based on genetic neural networks |
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CAI Guo-qiang1, XING Zong-yi2, PAN Li-sha3, CHENG Xiao-qing1, Qin yong1
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1.State Key Lab of Traffic Control and Safety, Beijing Jiaotong University, 100044 Beijing, China;2.School of Mechanical Engineering, Nanjing University of Science and Technology, 210094 Nanjing, China;3.Vehicle center, Guangzhou Metro Corporation, 510320 Guangzhou, China
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| Abstract: |
| To solve the modelling problem of wheel-rail force, a modelling approach based on the genetic algorithm and radial basis function neural network method is proposed, which can predict the output of wheel-rail force using the input of track irregularities. In order to improve the accuracy of the designed neural network and relieve the computational burden, the centers, widths and weights of the neural network are determined using the maximum distance measure, the least square method and genetic algorithm, respectively. The simulation results indicate that the proposed method can predict wheel-rail force with high precision. |
| Key words: wheel-rail force track irregularity modelling neural network genetic algorithm |
