【作者】 吴海俊；

【导师】 毛保华； 孙全欣；

【作者基本信息】 北京交通大学 ， 系统理论， 2008， 硕士

【摘要】 安全和效率是铁路运输生产永恒的主题,尤其是近年来随着我国铁路事业的快速发展,铁路运输呈现重载、高速、高密度的特点,而这一切需要可靠性高的列车运行控制系统作保障。制动控制作为列车运行控制的一部分,对列车的安全性起着关键作用。现行的基于牵引计算理论的制动模式曲线和列车实际运行存在偏差,导致控制效果并不理想,因此如何构造更准确的制动控制模型成为研究的热点。复杂系统的建模仿真,是系统建模领域的关键问题。模糊神经网络理论融合了模糊系统和神经网络二者的优势,能较好地实现复杂系统建模。本文以复杂系统建模为背景,对模糊神经网络建模方法、适合于列车制动控制建模的模糊神经网络结构及其应用进行了研究。论文在借鉴国内外已有研究成果的基础上,围绕列车制动控制建模这一问题,主要研究了以下内容:1.本文从复杂系统建模的观点分析了列车制动控制中所存在的模糊性问题及其产生的原因,得出将模糊神经网络引入列车制动建模是可行的。2.本文重点研究了在制动控制系统特性不够清楚的前提下构造适合于列车制动控制的模糊神经网络结构模型。通过对标准的模糊神经网络结构进行改进,得到一个四层的改进型模糊神经网络,从理论上证明模型具有全局逼近的特性,并推导了参数对应的学习算法。然后运用改进的模型对列车制动过程进行了建模,从实际操纵的角度上分析并确定了模型的输入输出变量,使该模型更符合列车实际运行环境,这和以往的模型所采用的结构及所选用的变量不同。3.为避免传统意义上语言变量划分太细导致模糊规则数目过多,影响网络的学习速度和精度,本文将聚类算法引入模糊神经网络结构辨识中对数据进行分类,同时结合实际操纵,确定所需规则数,兼顾了数据建模和实际操纵两方面的特点。4.为了验证改进的网络结构用于列车制动控制建模的有效性,本文以一列货物列车为例进行建模和计算,数值计算结果表明改进的模糊神经网络模型具有运算速度快、精度较高的特点;通过运用Matlab中Simulink模块对列车制动过程进行仿真,证明改进的模型运用于列车制动控制的建模是可行的。更多还原

【Abstract】 Safety and efficiency are eternal themes for railway transportation, especially as the rapid development of China’s railway. In recent years, the railway transportation has shown heavy, high-speed, high-density characteristics, all which need a high reliable train control system as a guarantee. Braking control, as a part of the train control, plays a key role in train safety. But there are some errors between the braking mode curve based on train-traction-calculation theory and the actual operation of the train, which results in that the effect of the control is not good. Therefore, how to construct a more accurate train braking control model becomes a hot research in recent years.Complex systems modeling and simulation is a key issue in systems modeling field. Fuzzy Neural Network(FNN) integrated both advantages of fuzzy systems and neural networks, can better realize complex systems modeling. In this paper, the method of FNN modeling, FNN structure which is suitable for modeling of the train braking control and its applications have been studied, which is based on the background of complex systems modeling.This paper, based on the research at home and abroad, is on the issue of train braking control modeling, and the main research contents are as follows:1. The fuzzy problems existed in the train braking control and its causes have been analyzed from the perspective of complex systems modeling, and it is drawn that the FNN used in the train braking modeling is feasible.2. In this paper, it is focused on how to construct a suitable FNN model for train braking control under the premise that the characteristics of the braking control system is not clear enough. Then the standard FNN has been improved and an improved four layers fuzzy neural network was acquired with its learning algorithm deduced, which is proved that this model has the characteristics of the overall approach in theory .Afterwards, we used the improved model to the train braking process and determine the input and output variables from practical point of view on the manipulation, which made the model is more adapted to the actual operating environment. This is different from the previous model structure and the variables selected are also different.3. Traditionally, too many language variables often lead too many fuzzy rules, and they have impact on network learning speed and accuracy. To avoid it, K-means clustering algorithm has been introduced to the FNN structure identification and been used for the data classification. At the same time, the actual manipulation is also considered combined with the classification to determine the necessary rules. It both takes into account the actual data modeling and the characteristics of the manipulation.4. In order to verify the validity of the improved network structure used in the train braking control modeling, in this paper, a fright train was used as an example for modeling and calculation. The numerical results show that improved fuzzy neural network model has the characteristics of high speed and precision. Then we used the Matlab module- Simulink to simulate the train braking process, and it proved that the improved model used in the train braking control is feasible.更多还原