现代电子技术2016，Vol.39Issue(13)：93-98,6.DOI:10.16652/j.issn.1004-373x.2016.13.023
基于模拟退火菌群-RBF神经网络的甲醇净化CO2含量软测量模型
SA-BFO and RBF neural network based soft measurement model for CO2 content purified by methanol
摘要
Abstract
In order to solve the problems of the bacterial foraging optimization(BFO)algorithm easily falls into the local optimum,and the reverse direction during chemotaxis operation is uncertain,the characteristic of simulated annealing(SA)al⁃gorithm is used to propose the simulated annealing and bacterial foraging optimization (SA⁃BFO) algorithm. SA algorithm can reach the global optimal solution to the maximum extent while obtaining the local optimal solution. The improved algorithm is ap⁃plied to optimizing the RBF neural network,and establishment the soft measurement model for CO2 content purified by metha⁃nol. The simulation results show that the model has high accuracy and precision,and has a certain contribution value for greatly improving the methanol production.关键词
菌群算法/模拟退火/RBF神经网络/甲醇净化Key words
bacterial foraging optimization/simulated annealing/RBF neural network/methanol purification分类
信息技术与安全科学引用本文复制引用
王潇逸,张凌波,顾幸生..基于模拟退火菌群-RBF神经网络的甲醇净化CO2含量软测量模型[J].现代电子技术,2016,39(13):93-98,6.基金项目
中央高校基本科研业务专项资金上海市重点学科项目 （）
