Parametric Design of Elevator Car Wall Based on GA-SVM Method

Abstract

The elevator car wall parameters are the pivot of parameters during the elevator design. To meet the individualized requirements of elevator design, and reduce the labor cost during design, the parametric design of elevator car wall model has important research significance in the design of elevator car. In this paper, genetic algorithm is utilized to optimize the internal parameters of the support vector machine method in order to establish the elevator car wall parameter prediction model based on GA-SVM. Based on the actual design data of Shanghai General Elevator Company over the years, 100 sets of data were simulated and predicted. The experimental results indicate that the average absolute percentage error of GA-SVM method is only 0.92%, and the relative error is 2.62%. The prediction accuracy is much better than BP neural network method. Most importantly, the GA-SVM method can effectively reduce the traditional labor cost of the elevator car design. Therefore, it is of great significance to the simulation design and manufacturing of the elevator car prototype.