基于改进即时学习的海洋碱性蛋白酶菌体浓度广义预测控制

GPC of bacterial concentration of marine alkaline protease based on improved just-in-time learning

针对微生物发酵过程中普遍存在的时变性、非线性等问题,基于最小二乘回归算法和改进即时学习(JITL)策略设计出一种基质给进速率控制器。首先,通过发酵仪器采集数据形成历史数据库,再使用加权模糊C均值聚类(WFCM)算法对数据进行分类,使查询值到来时能快速建立基于JITL-LS-SVM的海洋碱性蛋白酶菌体浓度局部预测模型。同时,为了避免预测控制中求解非线性问题,采用泰勒线性化方法,并用广义预测(GPC)算法对海洋碱性蛋白酶菌体浓度进行预测控制。实验仿真表明:基于改进JITL的JITL-LS-SVM模型能够达到较好的控制效果,并且葡萄糖给进速率响应迅速,适合海洋碱性蛋白酶发酵过程中的基质流加补料控制。

Aiming at the problem that a matrix feed rate controller is designed based on least square(LS) regression algorithm and improved just-in-time learning(JITL)strategy to solve the problems of time-varying and non-linear in microbial fermentation.first, the data are collected by fermentation instruments to form a historical database, and then the weighted fuzzy C mean(WFCM)clustering algorithm is used to classify the data, so that the local prediction model for bacterial concentration of marine alkaline protease based on JITL-LS-SVM can be established quickly when the query value arrived.At the same time, in order to avoid solving nonlinear problems in predictive control, Taylor linearization method is used to predict and control the bacterial concentration of marine alkaline protease by generalized prediction control(GPC) algorithm.Experimental simulation shows that the JITL-LS-SVM model based on improved real-time learning can achieve better control effect, and the glucose feed rate response is rapid, which is suitable for the control of matrix flow and feeding during the fermentation of marine alkaline protease.