RBF-CSR-EHCGA model and its application to pulsed extraction

CHENG Biao; CHEN Dezhao; WU Xiaohua

您当前的位置：

首页 >

文章列表页 >

RBF-CSR-EHCGA model and its application to pulsed extraction

更新时间：2025-12-10

- RBF-CSR-EHCGA model and its application to pulsed extraction
- CIESC Journal Issue 7, Pages: 1271-1275(2005)
- 作者机构：
  
  浙江大学化学工程与生物工程学系,浙江,杭州,310027
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：25 July 2005，
  
  Published：25 July 2005
- 稿件说明：
移动端阅览
CHENG Biao, CHEN Dezhao, WU Xiaohua. RBF-CSR-EHCGA model and its application to pulsed extraction[J]. CIESC Journal, 2005, (7): 1271-1275.
DOI：

CHENG Biao, CHEN Dezhao, WU Xiaohua. RBF-CSR-EHCGA model and its application to pulsed extraction[J]. CIESC Journal, 2005, (7): 1271-1275. DOI：

摘要

径向基函数-循环子空间回归（RBF-CSR）是一种有效的非线性网络模型，以高斯条为基函数，性能更优，但其参数多，且难以选定，将显著影响模型性能.为此，本文提出基于优进策略的混合编码遗传算法（EHCGA），以不同的方式为各类参数编码，并引入确定性的Powell算子，提高全局搜优效率.EHCGA算法以模型预报性能为目标，优选参数，以此建立RBF-CSR-EHCGA模型，它的预报精度高、稳定性良好.已成功应用于回收己内酰胺的脉冲萃取过程建模，效果良好，明显优于其他网络模型，也优于近似机理模型.

Abstract

Radial basis function-cyclic subspace regression (RBF-CSR) approach is a rapid one-step modeling method

escaping the difficulty of ANN architecture design. ANN and multivariate analysis are integrated into this approach

but it is difficult to optimize the parameters of the model because the method is a mixed integer programming.A eugenic hybrid coding genetic algorithm（EHCGA）

which used different coding methods for different type of variables was proposed to give the optimal parameters of the RBF-CSR model. In EHCGA deterministic optimization method was added to standard GA based on eugenic strategy

which could greatly improve the convergence speed and accuracy of GA. RBF-CSR model optimized by EHCGA called RBF-CSR-EHCGA model was successfully applied to modeling recovery of caprolactam from waste water by using pulsed-sieve-plate extraction column.

关键词

Keywords

references

Views

155

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Flux prediction of micro-filtration devices based on genetic neural network

A new strategy of optimization control for increasing fermentation unit

Optimal design of water networks by using genetic algorithm based on criterion of maximal water reuse

Soft computing applied to gas-assisted injection molding process optimization

Prediction of reverse osmosis performance based on genetic algorithm and adsorption-diffusion model

Related Author

CUI Yanjie

LIU Xiaopeng

YU Guangyu

WANG Chun

WANG Zhan

ZHANG Canhui

SUN Guangmin

GUAN Wei

Related Institution

北京工业大学环境与能源工程学院

北京工业大学电子信息与控制工程学院

上海理工大学动力工程学院

浙江大学化工机械研究所，郑州大学国家橡塑模具工程研究中心，陕西理工学院材料科学与工程学院

浙江四通环境工程有限公司

⁰