The application of support vector machines and improved particle swarm optimization

Parameters selection of support vector machine is the key issue that impacts its accurate performance. A method for support vector regression machine with standard particle swarm optimization (SPSO) algorithm is proposed in this paper. Furthermore, in order to improve the performance of the SPSO algorithm, the concept of the particles´ average distance and fitness variance is proposed to make the efficiency of algorithm better. So, the improve algorithm was also applied in this paper. The two different models using SPSO and IPSO respectively were used to forecast the density of the acid-lead battery electrolyte. The experimental results indicate that both SPSO and IPSO have high prediction accuracy and efficiency. The time of the parametric searching by IPSO is obviously decreased to that of SPSO. The mean squared error (MSE) of the prediction model using SPSO is about 2.02056×10⁻⁴, Meanwhile, the MSE of the model using IPSO is only about 1.9324×10⁻⁴. So, the IPSO algorithm has more superior performance on convergence speed and global optimization.