SVM optimized scheme based PSO in application of engineering industry process

Aimed to the problem that it is hardship to get real-time and on-line measuring parameters in wood drying process, a novel PSO-SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to improve the nonlinearity caused by ambient temperature and other disturbance factors is presented. Support vector machines (SVM) based on statistical learning theory and structural risk minimization is proposed to deal with these problems. However, the model complexity and generalization performance of support vector machines (SVM) depend on a good setting of the three parameters (epsiv,c,gamma). In this paper, the particle swarm optimization is applied to optimize the parameters (epsiv,c,gamma) at the same time. Based on the proposed method, both PSO-SVM and SVM models are established and implemented to estimate lumber moisture content value in wood drying process. The result of comparative analysis is given. Experimental results show that solutions obtained by PSO-SVM training seem to be more robust and better generalization performance compared to SVM training.