LS-SVM based soft sensoring

An improved least squares support vector machine (LS-SVM) approach was proposed to overcome the drawback of ldquolosing sparsityrdquo in original LS-SVM. At the same time, real-coded genetic algorithm (RC-GA) was introduced to solve the difficult problem of parameters selection in LS-SVM. By discarding most data points with too large or too small training errors in the trained LS-SVM model, a more sparse and anti-noise LS-SVM model was obtained. The parameters selection of LS-SVM was regard as an optimization problem. The objective function of the optimization problem was established. And a RC-GA with high global searching capability was used to search the optimal parameters of LS-SVM. Both simulation and experiment results demonstrate the success of the improved LSSVM and the effectiveness of RC-GA parameters optimization method.