人工神经网络在发动机建模中的应用研究

摘要

In order to solve the accuracy problems of engine modeling and curve fitting, the BP neural network was introduced which applied to engine modeling and curve fitting. The neural networks method and the least square method were used respectively to simulate and practice the external characteristic and universal characteristics of engine. On the basis of Matlab/Simulink modeling simulation, the curve fitting precision and error were evaluated. The simulation results show that the neural networks method has higher curve fitting precision and simplifies the calculation, which means a lot to the fuel economy and dynamic property of the vehicle. Further more, it affords the theory base to find the best matching strategy between engine and the transmission system of vehicle.%为解决发动机建模和数据拟合准确性的问题,介绍了人工神经网络BP算法的基本概念,并将其应用到发动机建模和数据拟合当中.采用神经网络法和最小二乘法,分别对发动机的外特性和万有特性进行了模拟和训练,在Matlab/Simulink仿真模型的基础上,对其拟合精度和误差进行了评价.仿真结果表明,神经网络法具有较高的拟合精度,而且计算方便,对研究车辆的动力性和燃油经济性的可信程度具有重要意义,可为实现发动机与车辆传动系统共同工作的动力匹配奠定一定的理论基础.