叠加支持向量机及其在醋酸精馏软测量中的应用

为了提高粒子群算法搜索精度和避免陷入局部最优，提出了一种改进的粒子群优化算法。一方面引入平均最好位置调整速度，使粒子可以利用更多的信息决策自己的行为；另一方面对引入的平均最好位置进行小波变异，增加算法的种群多样性。仿真实验结果表明：改进的粒子群算法具有寻优能力强、搜索精度高、稳定性好等特点。

A Simple Modified Multi kernel SVR and Its Application in Soft Sensor Computing of An Industrial Acetic Acid Distillation System

Aiming at the problem that the sparsity of data in the high dimensional input space will lead to the biased estimation of support vector regression (SVR), an additive SVR is proposed in this paper. This algorithm is realized via the addition of the separated input spaces after kernelization. Thus, the curse of dimensionality can be overcome by the additive model such that the bias can be reduced for high dimensional regression problem. Moreover, a simplified quadratic programing (QP) formulation of SVR is proposed for easily constructing the additive SVR model. Finally, the proposed method is employed to predict the concentration of HAC in the bottom outlet. It is shown from the experimental results that the additive SVR model can attain better predication performance than traditional SVR and least quare support vector regression (LS SVR).