基于RBF神经网络的柴油机控制模型辨识方法  

Research on identification method of diesel engine control model based on RBF neural network

作　　者：吴越 吴杰长[1] 常广晖[1] 刘树勇[1] WU Yue;WU Jie-chang;CHANG Guang-hui;LIU Shu-yong(College of Power Engineering,Naval University of Engineering,Wuhan 430033,China)

机构地区：[1]海军工程大学动力工程学院,湖北武汉430033

出　　处：《舰船科学技术》2022年第7期118-121,共4页Ship Science and Technology

基　　金：国家自然科学基金资助项目(51579242)。

摘　　要：针对柴油机电子调速器设计开发中需解决的高实时性模型建模问题,对基于RBF神经网络的柴油机建模方法进行研究,在Matlab/Simulink环境设计辨识模型和算法,以PA6柴油机为例进行了模型辨识实验验证。结果表明,本文方法具有逼近精度高、响应速度快等优点。For electronic governor of diesel engine design and development of model-based modeling problem need to solve the high real-time capability of model, the modeling method for diesel engine based on RBF neural network were studied. Under the environment of Matlab/Simulink, the identification model and the algorithm are designed. For PA6 diesel engine as an example for the model identification test, shows that the method has the advantages of high approximation accuracy and short response speed.

关 键 词：柴油机模型 系统辨识 RBF神经网络 

分 类 号：U672[交通运输工程—船舶及航道工程]