| 基于神经网络建模和遗传算法的重油脱盐系统优化研究 |
| |
| 引用本文： | 刘一凡,吴岳棉,胥布工,黄道平.基于神经网络建模和遗传算法的重油脱盐系统优化研究[J].计算机与应用化学,2003,20(4):528-532. |
| |
| 作者姓名： | 刘一凡 吴岳棉 胥布工 黄道平 |
| |
| 作者单位： | 1. 肇庆学院电子信息工程系,广东,肇庆,526061
2. 华南理工大学自动化科学与工程学院,广东,广州,510640 |
| |
| 摘 要： | 概述了重油脱盐系统的BP神经网络建模以及基于遗传算法的系统优化过程，将遗传算法与惩罚函数法相结合应用于约束优化的问题，改善了遗传算法的局限性。同时为了将不等式约束优化问题转化为单目标优化问题，对惩罚函数法进行了改进。结果表明：此方法可以有效解决静电脱盐问题。
|
| 关 键 词： | 神经网络 建模 遗传算法 重油 脱盐系统 优化 惩罚函数法 |
| 文章编号： | 1001-4160(2003)04-528-532 |
| 修稿时间： | 2003年4月21日 |
A study of the optimization of the desalting system of heavy oil based on the neural network modeling and the genetic algorithms |
| |
| LIU Yi-Fan,WU Yue-Mian,XU Bu-Gong,HUANG Dao-Ping.A study of the optimization of the desalting system of heavy oil based on the neural network modeling and the genetic algorithms[J].Computers and Applied Chemistry,2003,20(4):528-532. |
| |
| Authors: | LIU Yi-Fan WU Yue-Mian XU Bu-Gong HUANG Dao-Ping |
| |
| Abstract: | By applying the combination of the genetic algorithms and the penalty function to the restriction optimization problem, we can improve the limitation of the genetic algorithms. This paper introduces the neural network modeling of the desalting system of heavy oil and the system optimization process based on the genetic algorithm. At the mean time, the penalty function was improved by transmuting the inequality constrained optimization problem into single target optimization one. The result shows that this method can effectively solve the problem of static electricity desalting. |
| |
| Keywords: | back propagation neural network genetic algorithms penalty function desalting system of heavy oil |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
