Research on Classification Mining of Tank Driving Simulation Training Data

Abstract

Training with tank driving simulator is an important method to improve equipment operation skills. In view of the deficiency that it is difficult to find knowledge and rules from complex training data by statistical analysis method in the past driving simulation training, this paper proposes CSAGA-LSSVM algorithm to analyze tank driving simulation training data. Selecting key points to quickly generate shapelets, reducing the number of candidate shapelets; combining shapelets according to distance and time interval to enhance the ability of feature identification; designing adaptive genetic algorithm to dynamically adjust the probability of crossover and mutation to find the optimal parameter solution of least squares support vector machine. The algorithm is applied to the classification mining of shift operation data from a certain tank driving simulator to extract the operation characteristics of personnel.