PM2.5 Prediction of Innovation Priority Discrete Nonlinear Gray Model Based on Gray Wolf Optimization Algorithm

PM2.5 is one of the main factors air pollution, so prediction great significance. For this reason, innovation priority discrete nonlinear gray model based on wolf optimization algorithm established, which principle in system principle. Try to optimize model, and use solve parameters. First, basic theory proposed. On basis, used improve cumulative generation sequence, with parameters defined. Finally, using minimum error criterion, Take monthly data daily Mianyang City, Chengdu Zigong City Panzhihua Sichuan Province as examples. Apply perform forecast analysis, calculate absolute average percentage between predicted value observed Error, compare traditional model. The analysis shows that established has achieved good results, verifies practicability reliability proposed