Support vector regression based on multi-scale wavelet kernel has strong robustness and good generalization ability

but it is critical for it to choose appropriate model parameters.Obviously

the multi-scale kernel method has more difficulty in model selection than the single kernel approach.This paper proposed an approach about how to develop support vector regression based on mutli-scale wavelet kernel.It applied quantum clustering to data partition in order to determine the scale parameter of the multi-scale kernel

resorted to the support vector data description algorithm to calculate the kernel width of the corresponding data points

and then used cultural algorithms to optimize the kernel width and the remaining parameters.The results showed that the multi-scale kernel method outperformed the single wavelet approach and the Gaussian method.The experiments about three regression data sets—Boston housing

Bodyfat and Santa demonstrated that in contrast with the Gaussian approach

the present multi-scale wavelet support vector regression made the mean squared error of test sets decrease by 6.8%

62.0% and 91.3%

respectively.Meanwhile

the proposed approach exhibited good generalization ability for propylene concentration estimation in the bottom byproduct of propylene fractionation tower.It not only enabled the model output of training set for propylene concentration to show little difference with the actual output

but also made the relative error of test set down to 0.211.Compared with other methods

it had the minimal prediction error.