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

XIONG Weili; YAO Le; XU Baoguo

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

更新时间：2025-12-10

- Chaos least squares support vector machine and its application on fermentation process modeling
- CIESC Journal Issue 12, Pages: 4585-4591(2013)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2013.12.047
  CLC： P183
- Online First：05 December 2013，
  
  Published：05 December 2013
- 稿件说明：
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XIONG Weili, YAO Le, XU Baoguo. Chaos least squares support vector machine and its application on fermentation process modeling[J]. CIESC Journal, 2013, (12): 4585-4591.
DOI：

XIONG Weili, YAO Le, XU Baoguo. Chaos least squares support vector machine and its application on fermentation process modeling[J]. CIESC Journal, 2013, (12): 4585-4591. DOI： 10.3969/j.issn.0438-1157.2013.12.047.

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。

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.

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