Research on Improvement of PSO and CNN in Intelligent Classification Technology of Coal Mine Safety Risk

Abstract

This paper mainly studies the coal mine safety risk, including the classification, management strategy of coal mine safety risk and the development of risk management information technology. For the management of coal mine safety risks, it is very effective to formulate a safety risk classification management system, which can find and manage various safety hazards and risks as soon as possible, and promote efficient and safe operation. Nowadays, people increasingly take safety issues seriously in the coal industry, so the development of safety risk classification and control system has become an important safety management tool. To solve the slow convergence rate of CNN algorithm, this paper uses PSO to improve the error back propagation of CNN by using the training parameters and error functions of CNN as PSO particles and fitness functions respectively. The experimental simulation of AR face database in gender identification verifies that the improved algorithm has fast convergence rate and high recognition accuracy.