基于微粒群优化算法和支持向量机的软测量建模

Soft Sensor Modeling Based on Particle Swarm Optimization Algorithm and Support Vector Machine

在分析基本微粒群优化算法(PSO)和支持向量机(SVM)原理的基础上,采用带有末位淘汰机制的微粒群优化算法优化支持向量机的参数,建立了延迟焦化装置粗汽油干点软测量的微粒群支持向量机模型。该方法利用支持向量机结构风险最小化原则和PSO算法快速全局优化的特点,用于软测量建模。仿真实验表明:所建模型的泛化性能较好,模型具有较高的精度。

On the basis of analyzing the particle swarm optimization （PSO） algorithm and support vector machine （SVM）, this paper applies the PSO algorithm with last out mechanism to optimize the parameters of SVM. Then, the PSO-SVM model about a practical soft-sensor of gasoline endpoint of delayed coking plant is constructed. The method takes advantages of the minimum structure risk of SVM and the quickly globally optimizing ability of PSO for soft sensor modeling. The simulation results show that the model has effective generalization performance and higher precision.