Genetic Optimization Method of Pantograph and Catenary Comprehensive Monitor Status Prediction Model Based on Adadelta Deep Neural Network

Abstract

The status of the Pantograph and Catenary is the guarantee for the safe operation of the railway. However, the traditional Pantograph and Catenary status judgment efficiency is not satisfactory, and it is impossible to timely repair the catenary, which may lead to the greater economic loss. In this paper, a new GA-ADNN-based (genetic algorithm-Adadelta deep neural network-based) optimization method for the prediction model for catenary comprehensive pantograph and catenary monitor (CPCM) status is proposed. According to the status values of the CPCM parameters such as height, stagger, hard point, contact force, and height difference within span, the status value of the pillars in the catenary has been calculated by the analytic hierarchy process, and then the prediction model for predicting catenary CPCM status has been established and then optimized by genetic algorithm to avoid prediction model falling into local optimum. Finally, the CPCM test parameters of each pillar of the catenary in the actual example are input and the CPCM status value of the corresponding pillar is predicted. With the smallest prediction error found, the genetic algorithm is used for optimization, the optimal learning rate of the prediction model is 0.0559, and the optimal number of the hidden layer of the CPCM status prediction model is determined to be 14. The experimental results show the feasibility of GA-ADNN-based prediction model for predicting the catenary CPCM status, and that compared with the support vector machine and traditional artificial neural network prediction methods, the GA-ADNN-based prediction model has higher prediction precision and better generalization ability.

Keywords

• CPCM
• analytic hierarchy process
• catenary
• genetic algorithm
• GA-ADNN