Adaptive local learning based least squares support vector regression with application to online modeling for fermentation processes

LIU Yi; WANG Haiqing; LI Ping

您当前的位置：

首页 >

文章列表页 >

Adaptive local learning based least squares support vector regression with application to online modeling for fermentation processes

更新时间：2025-12-10

- Adaptive local learning based least squares support vector regression with application to online modeling for fermentation processes
- CIESC Journal Issue 8, Pages: 2052-2057(2008)
- 作者机构：
  
  工业控制技术国家重点实验室，浙江大学工业控制研究所,浙江,杭州,310027
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 August 2008，
  
  Published：05 August 2008
- 稿件说明：
移动端阅览
LIU Yi, WANG Haiqing, LI Ping. Adaptive local learning based least squares support vector regression with application to online modeling for fermentation processes[J]. CIESC Journal, 2008, (8): 2052-2057.
DOI：

LIU Yi, WANG Haiqing, LI Ping. Adaptive local learning based least squares support vector regression with application to online modeling for fermentation processes[J]. CIESC Journal, 2008, (8): 2052-2057. DOI：

摘要

提出一种基于自适应局部学习的最小二乘支持向量机回归（LSSVR）在线建模方法。考虑样本间的距离和角度信息以获得更全面合理的相似样本集，推导了采用快速留一法在线优化模型参数的准则，并给出了发酵过程在线自适应模型选择的策略。以链激酶流加发酵过程为例，验证了所提出算法能够从过程的第2批次开始，同时对活性菌体浓度和链激酶浓度进行较准确的在线预报，较普通的局部LSSVR等建模方法具有更高的预报精度和自适应性。

Abstract

A new online modeling method based on adaptive local learning and least squares support vector regression was proposed for nonlinear multi-input multi-output processes.Both distance measure and angle measure were used to evaluate the similarity between data

thus a more comprehensive and relevant set was constructed.In the present method

the model parameters were optimized online by using the fast leave-one-out criterion.In addition

an online adaptive model selection strategy for modeling of fermentation processes was developed.Simulations on a fed-batch streptokinase fermentation process showed that the active biomass and streptokinase concentrations could both be predicted simultaneously just from the second batch.The results also showed that the proposed method was more accurate and adaptive compared with the alternative modeling methods.

关键词

Keywords

references

Views

207

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A new approach for online adaptive modeling using incremental support vector regression

Key kernel network and its application to online modeling for fermentation processes

Local least squares support vector regression with application to online modeling for batch processes

Gradient algorithm for selecting hyper parameters of LSSVM in process modeling

Performance analysis and application of thermal and humidity control system cascade driven by waste heat

Related Author

WANG Ping

TIAN Huage

TIAN Xuemin

HUANG Dexian

LIU Yi

WANG Haiqing

LI Ping

TAO Shaohui

Related Institution

中国石油大学(华东)信息与控制工程学院

清华大学自动化系

浙江大学工业控制研究所，工业控制技术国家重点实验室

浙江大学化学工程与生物工程学系

⁰