Study On Pre-warning And Prediction Of Coal And Gas Outburst Based On Intelligent Information System

This paper analyzes the warning and prediction theory model based on comprehensive causation. Various hypotheses based on gas geological theory thought that coal and gas outburst is the result of joint action of crustal stress, gas pressure and coal body structure. It only gave explanation to the phenomenon, but could not work out unified and complete outburst theory. This paper establishes warning and prediction theory of serious coal and gas accident through the revelation of its causation chain and complicated mechanism.This paper makes in-depth comparison of PSO and GA (Genetic Algorithm) and integrates PSO and NN. For the insuperable problems of BP algorithm based on gradient descent like falling into local extremum and low convergence speed, it brings the PSO into the optimization of connection weight of NN and applies it to the prediction of serious coal and gas accidents. The simulation result proves that the algorithm is easy to be realized and with fast convergence speed.Making use of wavelet soft-threshold de-noising method, we de-nose according to the gas concentration time series of the working place obtained from KJ98 observation system of Luling Coal Mine. Through the adjustment of insensitive parameter e of support vector machine, we establish prediction model for gas concentration time series and analyze the influence of different parameters to prediction precision. Based on PSO, we optimize gas concentration model of support vector machine and obtain optimal pair parameters through the calculation of simulation result. Combined with prediction model of optimal parameter to gas concentration time series, we also analyze e - SVR parameter and prediction error, and makes detail comparison of the influence of training sample size to prediction precision.This paper also makes detail study of fuzzy logical system and NN, and establishes D-FNN warning model of gas outburst. The model can show qualitative knowledge and has capacity in self-learning and processing of quantitative data. It passes the verification of simulation experiment and realizes the prediction of gas outburst.