Evaluation of Electrostatic Safety Based on Support Vector Machines Optimized by Genetic Algorithm in Industry

摘要

Electrostatic ignition is one of primary causes fire and detonation in industry, so it is important to control and {he reducing electrostatic fire that appraising electrostatic safety of industry concerning the influence factors of electrostatic fire. In view of complexity of industry environment and multiplicity of the influence factors electrostatic safety, support vector machines is used to establish the evaluation model of electrostatic safety. For support vector machines has excellent performance in generalization and optimization. The auto-adaptive genetic algorithm that is improved by Auto-adaptive crossover and mutation probability is used to optimize support vector machines parameters. The superiority and the feasibility are proved through the simulation.