Quantum-inspired evolutionary tuning of SVM parameters
受限空间细水雾作用下烟气温度变化规律研究

Common used parameters selection method for support vector machines (SVM) is cross-validation, which needs a long-time complicated calculation. In this paper, a novel regularization parameter and kernel parameter tuning approach of SVM is presented based on quantum-inspired evolutionary algorithm (QEA). QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of least squares support vector machines (LS-SVM) can be adjusted using quantum-inspired evolutionary optimization. Classification and function estimation are studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the proposed approach can effectively tune the parameters of LS-SVM , and the improved LS-SVM with wavelet kernel can provide better precision.