Research on Noise Elimination of Coal Mine Gas Data Based on GA-LSSVR

Aiming at the problem that the coal mine gas data generally contain noise,a data denoising algorithm based on least squares support vector regression machine which was optimized by the genetic algorithm (GA-LSSVR) was proposed.With LSSVR,the optimal solution was obtained by solving the quadratic programming problem with only one equality constraint,and this thus improved the shortcoming of local optimum by wavelet denoising.However,since LSSVR also has the disadvantage of slow convergence rate,the genetic algorithms (GA) was used to optimize the LSSVR so as to improve the convergence rate of the algorithm.First,the abnormal data and the missing data processing was made on the the time series of gas concentrations in a coal mine,then GA-LSSVR was used for modeling training.The simulation experiment results showed that GA-LSSVR can effectively eliminate the noise,avoid the occurrence of data distortion and separate the effective signals as compared with the wavelet denoising method.Through the calculation,GA-LSSVR can reduce the root mean square error of the input and output by 0.002 94,relatively reduced by 34.59%,the noise eliminating effect was better;GA-LSSVR could obviously shorten the running time of the program and improve the running efficiency as compared with LSSVR method.