期刊文献+

计算机辅助设计在胎面胶损耗因子优化中的应用研究

Application of Computer-aided Design in Optimization on Loss Factor of Tread Compound
下载PDF
导出
摘要 以胎面胶为研究对象,应用拉丁超立方抽样策略合理安排试验获得试验数据,以硬度和300%定伸应力为约束条件、以损耗因子为优化目标,采用支持向量机分别建立损耗因子、硬度和300%定伸应力模型。应用遗传算法在硬度和300%定伸应力约束条件下对损耗因子进行优化,得到近似最优值。结果表明,数值优化结果与试验结果基本一致,证明该方法可行。 Latin-Hypercube simulation was carried out by using the test data from the designed ex- periments of tread compound. Then support vector machine was used to build the models of loss fac-tor,hardness and modulus at 300% elongation, respectively, where the loss factor was optimization objective, and hardness and modulus at 300% elongation were applied as the constraint conditions. The approximate optimal value of loss factor under these constraint conditions was obtained by genetic algorithm. The experimental results were in good agreement with the simulated results, which demon-strated that the simulation method was effective and could be put into practical use.
出处 《橡胶工业》 CAS 北大核心 2013年第5期275-278,共4页 China Rubber Industry
关键词 轮胎 胎面胶 损耗因子 支持向量机 遗传算法 tire tread compound loss factor support vector machine genetic algorithm
  • 相关文献

参考文献11

  • 1刘练,张永平,韦邦风.均匀设计法在工程机械轮胎胎面胶配方设计中的应用[J].橡胶工业,2009,56(9):554-556. 被引量:2
  • 2张玲霞,魏传.软测量建模方法探讨[J].琼州大学学报,2004,11(5):15-18. 被引量:3
  • 3李波,徐泽民,李方,张新申.试验设计与优化[J].中国皮革,2003,32(1):26-28. 被引量:12
  • 4A van Griensven, Meixner T. A Global Sensitivity AnalysisTool for the Parameters of Multi-variable Catchment Models[J].Journal of Hydrology, 2006,324 : 10-23.
  • 5Vapnik V N. Statistical Learning Theory[M]. New York:John Wiley Sons Inc.,1998:86-88.
  • 6Vladimir Cherkassky, Yunqian Ma. Practial Selection of SVMParameters and Noise Estimation for SVM Regression [J].Neural Networks,2004( 17) : 113-126.
  • 7王雷,张瑞青,盛伟,徐治皋.基于支持向量机的回归预测和异常数据检测[J].中国电机工程学报,2009,29(8):92-96. 被引量:60
  • 8Vapnik V N. The Nature of Statistical Learning Theory[M]. New York:Springer, 1999 : 104-106.
  • 9Chang C C, Lin C J. LIBSVM: A Library for Support VectorMachines[J]. ACM Transactions on Intelligent Systems andTechnology,2011,2(3) :No. 27.
  • 10Bi J,Bennett K P. A Geometric Approach to Support VectorRegression[J], Neuro Computing,2003,55 : 79-108.

二级参考文献37

共引文献179

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部