摘要
为了确定各因素对惯性分离室的性能的影响,以及寻找最优的参数组合,提出了一种新的BP神经网络的改进方法,对联合收割机惯性分离室的吸运系统压力损失的实验数据进行了拟合,并与二次回归模型方法进行比较。结果表明,改进的BP神经网络的拟合精度明显优于二次回归的拟合精度;同时,通过BP神经网络的优化方法求取了4个参数的最优组合值,为惯性分离室的性能研究提供了一种新的方法。
To determine the effects of various factors on the performance of the inertia separation chamber,and to find the optimal combination of parameters,this paper proposes an improved BP neural network method that had fitted the experimental data of suction system pressure drop in the inertia separation of combine harvester. Compared with the quadratic regression,the results showed that the fitting precision based on improved BP neural network was superior than quadratic regression. The value of the optimal combination of the four parameters was calculated by optimizing BP neural network. This paper provides a new method for the study of the performance of the inertia separation chamber.
出处
《农机化研究》
北大核心
2014年第9期42-46,共5页
Journal of Agricultural Mechanization Research
基金
国家自然科学基金项目(31071331)
关键词
联合收获机
惯性分离室
BP神经网络
二次回归
chamber
inertia separation
combine harvester
BP neural network
quadratic regression
