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

doi:10.3969/j.issn.0438-1157.2013.12.047

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

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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).

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

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）。

Abstract

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

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

CLC Number:

TP183

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.

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.

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

混沌最小二乘支持向量机及其在发酵过程建模中的应用

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