文献类型：期刊文献

中文题名：增强型微粒群优化算法及其在软测量中的应用

英文题名：Enhanced particle swarm optimization and its application in soft-sensor

作者：陈国初[1];俞金寿[1]

机构：[1]华东理工大学自动化研究所,上海200237

年份：2005

卷号：20

期号：4

起止页码：377

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD;;EI(收录号：2005229136649);Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

基金：教育部博士点专项基金项目(20030251003).

语种：中文

中文关键词：微粒群优化;增强型微粒群优化;神经网络;软测量

外文关键词：Artificial intelligence;Computer simulation;Fluid catalytic cracking;Gas fuel measurement;Optimization

摘要：对微粒群优化算法(PSO)进行分析,提出一种增强型微粒群优化算法(EPSO).用EPSO和PSO对几种常用函数的优化问题进行测试比较,结果表明EPSO比PSO更容易找到全局最优解,优化效率和优化性能明显提高.将EPSO用于催化裂化装置主分馏塔粗汽油干点软测量,建立了基于EPSO算法的粗汽油干点神经网络软测量模型.研究结果表明,基于EPSONN的软测量模型比基于BPNN的软测量模型具有更高的精度和更好的性能.
An enhanced particle swarm optimization algorithm (EPSO) is proposed based on the analysis of PSO. Both EPSO and PSO are used to resolve several well-known and widely used test function optimization problems. Results show that EPSO has greater efficiency, better performance and more advantages in many aspects than PSO. Then, EPSO is applied to train artificial neural network (NN) to construct a practical soft-sensor of gasoline endpoint of main fractionator of fluid catalytic cracking unit. The obtained results show that the proposed method is feasible and effective in soft-sensor of gasoline endpoint.