化工学报2018，Vol.69Issue(3)：1221-1227,7.DOI:10.11949/j.issn.0438-1157.20170598
基于改进PSO-RBFNN的海洋蛋白酶发酵过程软测量
Soft-sensing modeling of marine protease fermentation process based on improved PSO-RBFNN
摘要
Abstract
Some key parameters in the fermentation process of marine protease (MP) are difficult to be detected online. There is the existence of large time-delay and easily stained bacteria in off-line measurement. A soft sensor modeling method based on the improved PSO-RBFNN in the MP fermentation process was proposed. Firstly, exponential decreasing inertia weight (EDIW) strategy was used to improve PSO algorithm, and overcome the disadvantages that PSO with fixed inertia weight and adaptive inertia weight is easy to fall into the local minimum, the convergence rate is slow in late evolution and the global search ability is weak. Then the improved PSO algorithm was used to optimize the connection weight of RBFNN, and the RBFNN topology was successively determined. Finally, the RBFNN soft sensor model was constructed according to the input/output vector of MP fermentation process. The simulation results showed that the training time of the EDIW-PSO-RBFNN model was reduced by at least (about) 40%, and the prediction accuracy of model was improved by more than 3%.关键词
海洋蛋白酶/改进粒子群算法/径向基神经网络/软测量模型Key words
marine protease/improved PSO algorithm/RBF neural network/soft sensing model分类
信息技术与安全科学引用本文复制引用
朱湘临,凌婧,王博,郝建华,丁煜函..基于改进PSO-RBFNN的海洋蛋白酶发酵过程软测量[J].化工学报,2018,69(3):1221-1227,7.基金项目
国家自然科学基金面上项目(41376175) （41376175）
江苏省自然科学基金项目(BK20140568,BK20151345) （BK20140568,BK20151345）
江苏省高校自然科学研究面上项目(14KJB510007) （14KJB510007）
江苏高校优势学科建设工程资助项目(PAPD).supported by the National Natural Science Foundation of China(41376175)and the Natural Science Foundation of Jiangsu Province(BK20140568,BK20151345). （PAPD）
