摘要
为了提高支持向量机算法应用在行驶工况识别上的准确率,提出一种基于遗传算法优化策略。基于主成分分析理论对实车采集的4种典型城市工况载荷谱数据提取特征参数,并以此作为识别模型的输入参数,然后通过网格搜索法确定参数寻优空间,再由遗传算法在此范围内精确寻优。仿真试验结果显示,运用这种基于遗传算法优化支持向量机建立的识别模型分类识别精确度比之前提高了3.44%。
In order to improve the accuracy of the support vector machine algorithm applied to the driving condition recognition,an optimization strategy of genetic algorithm was put forward.Based on the principal component analysis theory,the characteristic parameters were extracted from the load spectrum data collected in 4 typical urban working conditions and used as the input parameters of the recognition model.Then the parameter optimization space was determined by the grid search method and the optimal value within the range could be found by the genetic algorithm.The simulation experiment results show that the recognition accuracy can improve by 3.44%based on the recognition model through the optimization of support vector machine with the genetic algorithm.
作者
董小瑞
武雅文
张志文
李晓杰
DONG Xiaorui;WU Yawen;ZHANG Zhiwen;LI Xiaojie(School of Energy and Power Engineering,North University of China,Taiyuan 030051,China)
出处
《车用发动机》
北大核心
2021年第2期13-17,共5页
Vehicle Engine
基金
山西省应用基础研究计划(201901D211208)。
关键词
行驶工况
识别
遗传算法
支持向量机
参数优化
driving condition
recognition
genetic algorithm
support vector machine
parameter optimization
