Hui Wu’s research while affiliated with Institute of Electrical Engineering, Chinese Academy of Sciences and other places

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.