Experiment statistical method and genetic algorithms based optimization method are used to obtain the optimum differential gear ratio for heavy truck that provides best fuel consumption when changing the working condi...Experiment statistical method and genetic algorithms based optimization method are used to obtain the optimum differential gear ratio for heavy truck that provides best fuel consumption when changing the working condition that affects its torque and speed range. The aim of the study is to obtain the optimum differential gear ratio with fast and accurate optimization calculation without affecting drivability characteristics of the vehicle according to certain driving cycles that represent the new working conditions of the truck. The study is carried on a mining dump truck YT3621 with 9 for- ward shift manual transmission. Two loading conditions, no load and 40 t, and four on road real driving cycles have been discussed. The truck powertrain is modeled using GT-drive, and DOE -post processing tool of the GT-suite is used for DOE analysis and genetic algorithm optimization.展开更多
The laser gyro is most su it able for building the strap down inertial navigation system (SINS), and its acc uracy of attitude algorithm can enormously affect that of the laser SINS. This p aper develops three improv...The laser gyro is most su it able for building the strap down inertial navigation system (SINS), and its acc uracy of attitude algorithm can enormously affect that of the laser SINS. This p aper develops three improved algorithmal expressions for strap down attitude ut ilizing the angular increment output by the laser gyro from the last two and cur rent updating periods according to the number of gyro samples, and analyses the algorithm error in the classical coning motion. Compared with the conventional algorithms, simulational results show that this improved algorithm has higher precision. A new way to improve the rotation vector algorithms is provided.展开更多
在很多实际应用问题中,不确定性的存在对于优化问题的最优解的性能会产生影响。在求解不确定环境下的优化问题时,往往需要考虑解的鲁棒性。最优解的鲁棒性定义通常要考虑其局部邻域内所有解的表现。在多目标优化背景下,如何逼近鲁棒最...在很多实际应用问题中,不确定性的存在对于优化问题的最优解的性能会产生影响。在求解不确定环境下的优化问题时,往往需要考虑解的鲁棒性。最优解的鲁棒性定义通常要考虑其局部邻域内所有解的表现。在多目标优化背景下,如何逼近鲁棒最优帕累托前沿也是一件非常有挑战性的工作。已有的鲁棒多目标进化算法能够比较好地处理低维鲁棒多目标优化问题,即问题的决策变量维数不超过10,但对于高维鲁棒多目标优化问题的表现往往不好。提出了一种结合自编码器以及协同进化方法的多目标进化算法(Decomposition-based Multiobjective Evolutionary Algorithm Assisted by Autoencoder and Cooperative Coevolution,MOEA/D-AECC),用来解决可降维的高维鲁棒多目标优化问题。该算法利用两个不同种群分别优化原始多目标优化问题以及对应的鲁棒多目标优化问题。为提高算法处理高维问题的能力,该算法利用自编码器模型对高维数据进行降维,从而提取出高维数据的低维特征。通过重构这些低维特征来学习可靠的下降方向,之后沿着可靠的下降方向采样产生新解。最后,通过实验测试了MOEA/D-AECC算法在一组可降维的高维鲁棒多目标优化问题上的表现。实验结果表明,MOEA/D-AECC算法的寻优显著优于其他几种代表性的鲁棒多目标进化算法。展开更多
文摘Experiment statistical method and genetic algorithms based optimization method are used to obtain the optimum differential gear ratio for heavy truck that provides best fuel consumption when changing the working condition that affects its torque and speed range. The aim of the study is to obtain the optimum differential gear ratio with fast and accurate optimization calculation without affecting drivability characteristics of the vehicle according to certain driving cycles that represent the new working conditions of the truck. The study is carried on a mining dump truck YT3621 with 9 for- ward shift manual transmission. Two loading conditions, no load and 40 t, and four on road real driving cycles have been discussed. The truck powertrain is modeled using GT-drive, and DOE -post processing tool of the GT-suite is used for DOE analysis and genetic algorithm optimization.
文摘The laser gyro is most su it able for building the strap down inertial navigation system (SINS), and its acc uracy of attitude algorithm can enormously affect that of the laser SINS. This p aper develops three improved algorithmal expressions for strap down attitude ut ilizing the angular increment output by the laser gyro from the last two and cur rent updating periods according to the number of gyro samples, and analyses the algorithm error in the classical coning motion. Compared with the conventional algorithms, simulational results show that this improved algorithm has higher precision. A new way to improve the rotation vector algorithms is provided.
文摘在很多实际应用问题中,不确定性的存在对于优化问题的最优解的性能会产生影响。在求解不确定环境下的优化问题时,往往需要考虑解的鲁棒性。最优解的鲁棒性定义通常要考虑其局部邻域内所有解的表现。在多目标优化背景下,如何逼近鲁棒最优帕累托前沿也是一件非常有挑战性的工作。已有的鲁棒多目标进化算法能够比较好地处理低维鲁棒多目标优化问题,即问题的决策变量维数不超过10,但对于高维鲁棒多目标优化问题的表现往往不好。提出了一种结合自编码器以及协同进化方法的多目标进化算法(Decomposition-based Multiobjective Evolutionary Algorithm Assisted by Autoencoder and Cooperative Coevolution,MOEA/D-AECC),用来解决可降维的高维鲁棒多目标优化问题。该算法利用两个不同种群分别优化原始多目标优化问题以及对应的鲁棒多目标优化问题。为提高算法处理高维问题的能力,该算法利用自编码器模型对高维数据进行降维,从而提取出高维数据的低维特征。通过重构这些低维特征来学习可靠的下降方向,之后沿着可靠的下降方向采样产生新解。最后,通过实验测试了MOEA/D-AECC算法在一组可降维的高维鲁棒多目标优化问题上的表现。实验结果表明,MOEA/D-AECC算法的寻优显著优于其他几种代表性的鲁棒多目标进化算法。
文摘由于航空目标相对地面目标具有更快的运动速度、更广的运动范围,对航空目标的三维精确定位极具挑战性.本文提出了一种多传感器组网的航空目标三维定位算法,以两个高空无人飞艇各载一部光学传感器设备,无人机-艇载双基地两坐标雷达,多平台协同实现对航空目标的精确定位为研究背景,解决了由于各传感器量测维度欠完备、无法独立获得目标三维空间精确位置,导致传统点迹关联、目标定位方法失效等问题.首先,在空间对准的基础上提出了基于角度-距离两级点迹关联算法,实现多传感器缺维量测的有效关联;其次,通过目标引导点构建、椭球空间Nelder-Mead欧氏距离寻优、方位面空间投影,在各空间量测模型上确定目标初始定位点;最后,通过无迹变换和同源数据压缩得到目标精确定位点.仿真结果表明,该算法实现了缺维情况下雷达-双光学量测数据的稳定关联,且对航空目标的最优定位误差可达到115.7 m.
