摘要
在基于模糊神经网络结构的基础上,通过分析影响建立模糊神经网络模型的主要因素,建立了用于列车运行控制的控制器模型,并且给出了相关参数的辨识方法。以货物列车为仿真对象,采用带有动量因子的BP反向传播算法对整个控制模型进行了仿真。仿真结果表明,基于模糊神经网络的智能算法能够满足列车制动控制的安全性、准确性及舒适性的要求,将其运用于列车制动控制是可行的。
First, the main factors which affected the establishment of an FNN model were analyzed based on the structure of FNN. Then, a controller model which was used in train operation control was established. Furthermore, the identification method of the relevant parameters was given. The freight train was used as simulation objects, and the back-propagation algorithm with momentum factor was used to simulate the entire control model. The results show that the intelligent algorithm based on fuzzy neural network can meet the requirements of safety, accuracy, and comfort. Its application in train braking control is feasible.
出处
《交通与计算机》
2008年第1期55-58,共4页
Computer and Communications
基金
国家自然科学基金重点项目资助(批准号:60634010)
关键词
列车制动控制
模糊神经网络
模糊系统
反向传播算法
train brake control
fuzzy neural network fuzzy system
back-propagation algorithm
