摘要
针对氧乐果合成过程中温度控制具有参数时变、时滞后、非线性的特点,提出了一种基于改进粒子群算法的支持向量回归的建模方法;对于支持向量回归模型,3个参数(ε,C,γ)的选取很大程度上决定了其拟合的精度和泛化能力的好坏,采用改进的粒子群算法对参数(ε,C,γ)进行同时寻优,建立了改进的氧乐果合成过程PSO-SVR回归模型,该模型具有很好的学习能力和推广能力;实验结果表明,模型较好地体现了系统的动态特性,可用于氧乐果合成过程的模型预估控制,提高系统的控制品质。
In accordance with characteristics of parameter time--varying, nonlinear, time lag in omethoate synthesis process temperature control, an improved control algorithm based on particle swarm optimization and support vector regression is proposed in this paper. For sup- port vector regression, a good setting of the three parameters （ε,C,γ） have a huge impact on the model regression accuracy and generaliza- tion performance. The improved particle swarm optimization is applied to optimize the parameters （ε,C,γ） at the same time. An omethoate synthesis process model based on improved PSO--SVR is established and the model has good learning accuracy and generalization perform ance. The experimental results show that the model reflects dynamic characteristics of the system and can be used for predictive control of the omethoate synthesis process model to improve the control quality of the system.
出处
《计算机测量与控制》
2015年第4期1296-1298,共3页
Computer Measurement &Control
基金
国家自然科学基金资助项目(60774059)
关键词
改进的粒子群算法
支持向量回归
氧乐果合成
温度控制
improved particle swarm optimization
support vector regression
omethoate synthesis
temperature control
