智能预测模型在多粘芽孢杆菌发酵中的应用Research on paenibacillus polymyxafermentation of intelligent forecasting model
魏安静;田丽;凤权;
摘要(Abstract)：
多粘芽孢杆菌发酵是一个极其复杂的生物反应过程,其诸多参数具有动态非线性。传统的发酵实验都是在不断地尝试中进行的,如果能预测发酵过程中的主要参数则会大大提高实验效率。用一种智能预测模型,即将Kohonen网络、Elman神经网络和粒子群优化算法有机结合,可以预测发酵过程中的主要参数。该模型的仿真实验结果符合多粘芽孢杆菌发酵的动力学特点,实现了部分参数的有效预测。研究结果表明该智能预测模型不但能够综合各种单一预测模型的优点,而且能够随时间的推移其内在结构不断变化,适用于多粘芽孢杆菌发酵过程的参数预测和特性优化。相对于传统预测方法,提高了预测效率。
关键词(KeyWords)： 智能预测;多粘芽孢杆菌;发酵特性;建模;优化
基金项目(Foundation): 安徽高校省级自然科学研究项目(KJ2012B014,KJ2012A039)
作者(Author): 魏安静;田丽;凤权;
Email:
DOI:
参考文献(References)：
- [1]刘尧猛,马永军,杨美艳.改进型Elman神经网络发酵过程建模研究[J].计算机工程与应用,2009,45(32):240-243.
- [2]Niu,D P,Wang F L,Zhang L L,et al.Neural network ensemblemodeling for nosiheptide fermentation process based on partial leastsquares regression[J].Chemometrics and Intelligent Laboratory Sys-tems,2011,105(1):125-130.
- [3]田鹏.智能优化算法在发酵过程建模中的应用研究[D].江南大学,2009,35-44.
- [4]Ge H W,Qian F,Liang Y C,et al.Identification and control ofnonlinear systems by a dissimilation particle swarm optimization-based Elman neural network[J].Nonlinear Analysis:Real WorldApplications,2008,9(4):1345-1360.
- [5]He Q,Wang L.An effective co-evolutionary particle swarm optimiza-tion for constrained engineering design problems[J].EngineeringApplications of Artificial Intelligence,2007,20:89-99.
- [6]Becker T,Enders T,Delgado A.Dynamic neural networks as a toolfor the online optimization of industrial fermentation[J].Bioprocessand Biosystems Engineering,2002,24(6):51-56.
- [7]李金金,田雨波.粒子群优化算法边界条件研究[J].计算机工程,2011,37(24):173-179.
- [8]Kurt A,Oktay A B.Forecasting air pollutant indicator levels with ge-ographic models 3 days in advance using neural networks[J].ExpertSystems with Applications,2010,37(12):7986-7992.
- [9]隋青美,王正欧.发酵过程混合神经网络模型及其仿真[J].系统仿真学报,2002,14(4):415-417.
- [10]Brunelli U,Piazza V,Pignato L,et al.Three hours ahead previsionof SO2 pollutant concentration using an Elman neural based forecas-ter[J].Building and Environment,2008,43(3):304-314.
- [11]Kelo S,Dudul S.A wavelet Elman neural network for short-termelectrical load prediction under the influence of temperature[J].In-ternational Journal of Electrical Power&Energy Systems,2012,43(1):1063-1071.
- [12]王巧立,陈铁军.基于QPSO组合优化的发酵过程LS-SVM建模[J].计算机工程与设计,2011,1:285-288.
- [13]Wu Q,Law R.An intelligent forecasting model based on robustwaveletν-support vector machine[J].Expert Systems with Appli-cations,2011,38(5):4851-4859.
- [14]魏安静,田丽,凤权.基于级联网络的短期电力负荷预测研究[J].计算机仿真,2011,28(1):311-314.
- [15]凤权,汤斌,陈中碧.多粘芽孢杆菌发酵培养基优化及发酵特性研究[J].食品与发酵工业,2007,33(7):46-48.