[Support vector machine and optimized method for spectral analysis].

According to support vector machine based on the regularization theory, a small scale machine study theory was proposed to solve the problem of multi-gas analysis, which is mainly restricted by the lack of experimental samples. With its well nonlinear mapping ability, the training error was decided to be zero and global optimal parameters were obtained, hence the cross-sensitivity of spectrum is preferably eliminated. In multi-component gas analysis, the results show that the cross-sensitivity decreased to 1/81. A method based on genetic algorithm and cross-validation was proposed to solve the parameters selection of support vector machine(SVM), which still lacks theory support. The optimal structure parameters were achieved by genetic random search algorithm, the mean square error(MSE) 0. 018 of the spectrometer was achieved in 20th generation, and MSE decreased by multi-times in the fore generations. This hints that the genetic algorithm SVM is more efficient and has better generalizing ability.