混沌最小二乘支持向量机及其在发酵过程建模中的应用

doi:10.3969/j.issn.0438-1157.2013.12.047

化工学报 ›› 2013, Vol. 64 ›› Issue (12): 4585-4591.DOI: 10.3969/j.issn.0438-1157.2013.12.047

混沌最小二乘支持向量机及其在发酵过程建模中的应用

熊伟丽^1,2, 姚乐², 徐保国^1,2

1. 江南大学轻工过程先进控制教育部重点实验室, 江苏无锡 214122;
2. 江南大学物联网工程学院自动化系, 江苏无锡 214122

收稿日期:2013-08-16 修回日期:2013-08-23 出版日期:2013-12-05 发布日期:2013-12-05
通讯作者: 熊伟丽
作者简介:熊伟丽（1978- ），女，博士，副教授。
基金资助:
国家自然科学基金项目（21206053，21276111）；中国博士后基金资助项目（2012M511198）；江苏高校优势学科建设工程资助项目（PAPD）。

Chaos least squares support vector machine and its application on fermentation process modeling

XIONG Weili^1,2, YAO Le², XU Baoguo^1,2

1. Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, Jiangsu, China;
2. Department of Automation, College of IOT Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China

Received:2013-08-16 Revised:2013-08-23 Online:2013-12-05 Published:2013-12-05
Supported by:
supported by the National Natural Science Foundation of China (21206053，21276111)，the China Postdoctoral Science Foundation (2012M511678) and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

摘要/Abstract

摘要： 针对青霉素发酵过程的参数检测存在不确定因素，提出一种基于混沌最小二乘支持向量机的青霉素浓度预测方案。采用混沌优化算法对最小二乘支持向量机参数进行寻优，建立了一种混沌最小二乘支持向量机模型。首先，利用该模型对两种常规非线性函数曲线进行了仿真回归，结果表明，算法具有良好的建模精度；其次，基于Pensim仿真平台，运用文中方法预测青霉素发酵过程的产物量，实验仿真表明混沌优化算法具有良好的全局优化性能，在参数选择中可以有效避免陷入局部最小值，基于混沌优化的最小二乘支持向量机具有较高的建模精度。

关键词: 混沌, 最小二乘支持向量机, 建模, 青霉素

Abstract: For uncertainties of parameter detection in penicillin fermentation process,penicillin concentration prediction scheme by chaos least squares support vector machine is put forward.The LSSVM parameters were optimized by chaos optimization algorithm to set up Chaos-LSSVM model. Firstly,simulation is conducted for two kinds of nonlinear function curve.The results show that the algorithm has good precision of modeling.Secondly,taking the data of Pensim simulation platform to model penicillin concentration curve,predicting the product of the penicillin fermentation process.The results show that chaos optimization algorithm has a good global optimization performance,which prevents parameters from falling into local minimum,improving the prediction accuracy of the model.

Key words: chaos, least squares support vector machine, model, penicillin

中图分类号:

TP183

熊伟丽, 姚乐, 徐保国. 混沌最小二乘支持向量机及其在发酵过程建模中的应用[J]. 化工学报, 2013, 64(12): 4585-4591.

XIONG Weili, YAO Le, XU Baoguo. Chaos least squares support vector machine and its application on fermentation process modeling[J]. CIESC Journal, 2013, 64(12): 4585-4591.

参考文献 20

[1]	Sun Qi (孙琦).Analysis of characteristics of penicillin fermentation process and its control method[J].Technology Innovation and Application (科技创新与应用), 2012, 5 (12):29-30
[2]	Yan Weiwu (阎威武), Zhu Hongdong (朱宏栋), Shao Huihe (邵惠鹤).Soft sensor modeling based on support vector machines[J].Journal of System Simulation (系统仿真学报), 2003, 15 (10):1494-1496
[3]	Liu Yi (刘毅), Wang Haiqing (王海清).Modeling a penicillin fed-batch fermentation using least squares support vector machines[J].Chinese Journal of Biotechnology (生物工程学报), 2006, 22 (1):144-149
[4]	Vapnik V N.The Nature of Statistical Learning Theory[M].New York:Springer-Verlag, 1995
[5]	Suykens J A K, Vandewalle J.Least squares support vector machines classifiers[J].Neural Processing Letters, 1999, 10 (5):1032-1037
[6]	Aydin Ⅱhan, Karakose Mehmet, Akin Erhan.A multi-objective artificial immune algorithm for parameter optimization in support vector machines[J].Applied Soft Computing Journal, 2011, 11 (1):120-129
[7]	Yang Fuguang, Wang Ruling, Wang Yongqiang.Study on classification performance and parameter optimization of least squares support vector machines[J].International Journal of Advancements in Computing Technology, 2012, 4 (12):152-159
[8]	Bai Jing, Yang Lihong, Zhang Xueying.Parameter optimization and application of support vector machines based on parallel artificial fish swarm algorithm[J].Journal of Software, 2013, 8 (3):673-679
[9]	Xie Chunli, Shao Cheng, Zhao Dandan.Parameter optimization of least squares support vector machines and its application[J].Journal of Computers, 2011, 6 (9):1935-1941
[10]	Han Fang (韩芳), Wang Xinxiang (王新香), Wang Aimin (王爱民).Research on simulation and modeling on penicillin fermentation process[J].Computer Simulation (计算机仿真), 2013, 30 (4):361-364
[11]	Liu Guohai (刘国海), Zhou Dawei (周大为), Xu Haixia (徐海霞), Mei Congli (梅从立).Soft sensor modeling using SVM in fermentation process[J].Chinese Journal of Scientific Instrument (仪器仪表学报), 2009, 30 (6):1228-1232
[12]	Chen Ruqing (陈如清), Yu Jinshou (俞金寿).Soft sensor modeling based on particle swarm optimization and least squares support vector machines[J].Journal of System Simulation (系统仿真学报), 2007, 19 (22):5307-5310
[13]	Yang Yan (杨妍), Chen Ruqing (陈如清), Yu Jinshou (俞金寿).Soft sensor modeling based on chaos optimization algorithm and least squares support vector machines[J].Journal of East China University of Science and Technology:Natural Science Edition (华东理工大学学报:自然科学版), 2009, 35 (6):902-906
[14]	Yuan Xiaofang (袁小芳), Wang Yaonan (王耀南).Selection of parameters using chaos optimization algorithm[J].Control and Decision (控制与决策), 2006, 21 (1):111-117
[15]	Liang Ximing (梁昔明), Yan Gang (阎纲), Li Shanchun (李山春), Long Zuqiang (龙祖强).Nonlinear predictive control based on least squares support vector machines and chaos optimization[J].Information and Control (信息与控制), 2009, 39 (2):129-135
[16]	Liu Yi (刘毅), Wang Haiqing (王海清).Pensim simulator and its application in penicillin fermentation process[J].Journal of System Simulation (系统仿真学报), 2006, 18 (12):3524-3527
[17]	Hu Xinghua (胡行华), Gao Leifu (高雷阜).Effect of different chaostic sequences on seeking overall optimal solution[J].Journal of Liaoning Technical University (辽宁工程技术大学学报), 2008, 27 (4):629-631
[18]	Cui Chang (崔畅), Zhao Qiang (赵强).Research on improved chaos optimization algorithm[J].Science Technology and Engineering (科学技术与工程), 2007, 7 (3):307-313
[19]	Liu Yi (刘毅), Wang Haiqing (王海清).Modeling of the penicillin fermentation process via LSSVM based on Pensim simulator[J].Chemical Reaction Engineering and Technology (化学工程与工艺), 2006, 22 (3):252-258
[20]	He Xiaoran (贺晓冉), Chen Chen (陈宸), Kim Kwang Sok (金光石), Xiong Zhihua (熊智华).Model simulation of fed-batch penicillin fermentation and optimization of substrate feedrate[J].CIESC Journal (化工学报), 2012, 63 (9):2831-2835

混沌最小二乘支持向量机及其在发酵过程建模中的应用

Chaos least squares support vector machine and its application on fermentation process modeling

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 20

相关文章 15

编辑推荐

Metrics

本文评价

[1]	陈国泽, 卫东, 郭倩, 向志平. 负载跟踪状态下的铝空气电池堆最优功率点优化方法[J]. 化工学报, 2023, 74(8): 3533-3542.
[2]	邵伟明, 韩文学, 宋伟, 杨勇, 陈灿, 赵东亚. 基于分布式贝叶斯隐马尔可夫回归的动态软测量建模方法[J]. 化工学报, 2023, 74(6): 2495-2502.
[3]	周乐, 沈程凯, 吴超, 侯北平, 宋执环. 深度融合特征提取网络及其在化工过程软测量中的应用[J]. 化工学报, 2022, 73(7): 3156-3165.
[4]	王琨, 侍洪波, 谭帅, 宋冰, 陶阳. 局部时差约束邻域保持嵌入算法在故障检测中的应用[J]. 化工学报, 2022, 73(7): 3109-3119.
[5]	钱宇, 陈耀熙, 史晓斐, 杨思宇. 太阳能波动特性大数据分析与风光互补耦合制氢系统集成[J]. 化工学报, 2022, 73(5): 2101-2110.
[6]	高欢, 丁国良, 周发贤, 庄大伟. R410A制冷剂在润滑油中的动态析出特性的研究[J]. 化工学报, 2022, 73(3): 1054-1062.
[7]	赵文一, 匡以武, 王文, 张红星, 苗建印. 水平管内冷凝流动的稳定性[J]. 化工学报, 2021, 72(S1): 257-265.
[8]	梁旭鸣, 沈永超, 卫东, 郭倩, 高志. 基于直流内阻和交流阻抗特性的铝空气电池输出特性分析[J]. 化工学报, 2021, 72(8): 4361-4370.
[9]	姚源朝, 仇鹏, 许建良, 代正华, 刘海峰. 基于混合模型的气流床气化炉建模[J]. 化工学报, 2021, 72(5): 2727-2734.
[10]	石珊珊, 魏爱博, 张小斌. 液氮超声空化CFD模拟及实验研究[J]. 化工学报, 2021, 72(4): 1930-1938.
[11]	王晓慧, 王延江, 邓晓刚, 张政. 基于加权深度支持向量数据描述的工业过程故障检测[J]. 化工学报, 2021, 72(11): 5707-5716.
[12]	孟繁鑫, 孙佳宁, 周月, 高赞军, 程定斌. 飞机环控系统空气循环机仿真建模及试验校核[J]. 化工学报, 2020, 71(S1): 328-334.
[13]	徐百平, 刘彪, 刘尧, 谭寿再, 杜遥雪, 刘春太. 不同错列角三角形转子作用下的分布混合数值模拟[J]. 化工学报, 2020, 71(8): 3545-3555.
[14]	尹林子, 关羽吟, 蒋朝辉, 许雪梅. 基于k-means++的高炉铁水硅含量数据优选方法[J]. 化工学报, 2020, 71(8): 3661-3670.
[15]	黄健, 赵众. 延迟焦化加热炉热效率的机理建模与实时估计应用[J]. 化工学报, 2020, 71(7): 3140-3150.