文献类型：期刊文献

中文题名：基于改进云粒子群优化的模糊神经网络在甲醇合成转化率软测量中的应用

英文题名：Fuzzy Neural Network Based on Improved Cloud Particle Swarm Optimization and Its Application in the Soft Sensing of Methanol Synthesis Tower Conversion

作者：赵光柱[1];张凌波[1];顾幸生[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

年份：2013

卷号：39

期号：6

起止页码：697

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2011】；CSCD:【CSCD2013_2014】；

基金：中央高校基本科研业务费专项资金;国家"863"项目(2009AA04Z141);上海市重点学科项目(B504)

语种：中文

中文关键词：粒子群优化算法;云模型;模糊神经网络;甲醇

外文关键词：particle swarm optimization（PSO） ; cloud model; fuzzy neural network; methanol

摘要：针对基本PSO算法早熟、搜索精度不高与易陷入局部最优的缺点,结合云滴的随机性、稳定倾向性,提出了一种改进粒子群优化算法(ICPSO)。将改进算法用于模糊神经网络的参数优化,并应用于甲醇单程转化率建模中。仿真实验结果表明:该模型具有较高的精度和较好的泛化能力,能够实现甲醇转化率的实时监测。
By integrating the randomness and stable tendency of cloud droplets, this paper proposes an improved particle swarm algorithm （ICPSO） so as to overcome the premature convergence and easily plunging into the local optimization of the PSO algorithm. And then, the improved algorithm is utilized to optimize the parameters of the fuzzy neural network, which is further applied to the modeling of methanol conversion. The experiment results show that the proposed model has higher precision and better generalization ability, and can realize real-time monitoring of the methanol conversion.