URT train energy-saving scheme optimized on case intelligence using SRS and RBF

2018 3rd International Conference on Insulating Materials, Material Application and Electrical Engineering

材料科学;无线电电子学;电工学

Li, Jianyang^1,2 ; Wu, Hui^2 ; Wang, Ming^2

School of Computer Engineering, Hefei University of Technology, Hefei, China^1

School of Electrical Engineering, Zhenjiang Institute, Zhenjiang, China^2

关键词: CBR (case based reasoning);    Energy efficient;    Energy saving schemes;    Flexible system;    Impact factor;    Machine learning methods;    Train operations;    Urban rail transit;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/452/4/042040/pdf
DOI  :  10.1088/1757-899X/452/4/042040

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Great challenge of energy consumption in urban rail transit (URT) has been attracting much greater concerns for its more complicated impact factors. To find the energy-efficient train speed curve is the essential way for energy-saving propulsion, but it is a difficult task full of uncertainty knowledge involving in real-time train operation, which is a complicated CSP (constraint satisfaction problem) with so much inconsistent constrains that cannot be solved effectively. The paper proposes case intelligence based on CBR (case-based reasoning) to acquire the train operation preferences from the former stored cases, and constructs a flexible system integrated with efficient machine learning methods for synthesis-reasoning. The subsequent research indicates that similarity rough sets (SRS) and radial basis function network (RBF) can conquer the complexity and uncertainty of real problem, for the experimental results indicates that the hybrid system gives a fine performance as shown in real-time URT train operation.