BP神经网络和遗传算法在车用发动机上的运用  

Application of BP Neural Network and Genetic Algorithm in Vehicle Engine

作　　者：张奎 占刚[1,2] 毛卫秀 吴业强[1] 徐鸿宇 Zhang Kui;Zhan Gang;Mao Weixiu;Wu Yeqiang;Xu Hongyu(Guizhou Vocational Technology College of Electronics&Information,Kaili 55600;Guizhou University Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education,Guiyang 550025)

机构地区：[1]贵州电子信息职业技术学院,凯里556000 [2]贵州大学现代制造技术教育部重点实验室,贵阳550025

出　　处：《汽车文摘》2019年第9期57-62,共6页Automotive Digest

基　　金：贵州省科技支撑项目(黔科合支撑[2016]2334)

摘　　要：随着车用发动机的不断发展与完善,车用发动机相关系统已然成为一个复杂的非线性系统,传统的优化算法已经不适合在车用发动机上应用,暴露了其局限性,对于求解离散优化问题,多约束、多极值问题,传统优化算法已无法满足要求。由于具有独特的优点,BP神经网络和遗传算法逐渐在车用发动机上得到应用。因此,阐述了BP神经网络和遗传算法的原理,以及BP神经网络和遗传算法在车用发动机的性能提升、模型标定、故障诊断等领域的应用及研究进展。With the continuous development and improvement of vehicle engine,systems related to vehicle engine have become a complex nonlinear one.The application of traditional optimization algorithm is not suitable for vehicle engine,its limitations have been exposed.Regarding solving discrete optimization problems,multi-constraint and multiextremum problems,traditional optimization algorithm can not be satisfied.Because of the unique advantages BP neural network and genetic algorithm are gradually applied in automotive engine.Therefore,this paper describes the principle of BP neural network and genetic algorithm,as well as the application and research progress of BP neural network and genetic algorithm in the fields of vehicle engine performance improvement,model calibration,fault diagnosis and other fields.

关 键 词：BP神经网络 遗传算法 车用发动机 模型标定 性能提升 故障诊断 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]