Model Selection of Least Squares Support Vector Regression Using Quantum-Behaved Particle Swarm Optimization Algorithm

The selection for hyper-parameters is difficult and important to the performance of least squares support vector machines (LS-SVM). The existed parameters selection algorithms, such as the analytical, algebraic techniques and particle swarm optimization (PSO) algorithm, have their own shortcomings. In this paper, the problem of model selection for LS-SVM is discussed and a new method selecting the LS-SVM hyper-parameters is proposed based on the principles of the quantum-behaved particle swarm optimization (QPSO). The feasibility of this method is evaluated on data sets produced by sine function. Experimental results show that LS-SVM of QPSO-based hyper- parameters selection obtains better generalization capability and has more fast convergence speed than PSO-based hyper-parameters selection. Furthermore, the proposed method was applied to establish a soft- sensor model for content of Bisphenol A (CBPA) in rearrangement productive process. The results of real data simulation also show that this method is effective.