Soft-Sensing of Oil-Water Interface Based on Modified Particle Swarm Optimization Algorithm with BP Neural Network

被引：0
|
作者：
Duan, Yu-bo [1 ]
Feng, Ting-ting [1 ]
Shao, Ke-yong [1 ]
Yuan, Meng-yu [1 ]
机构：
[1] Northeast Petr Univ, Sch Elect & Informat Engn, Daqing 163318, Peoples R China
来源：
INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SOFTWARE ENGINEERING (AISE 2014) | 2014年
关键词：
Oil-water interface; Particle swam optimization algorithm; BP algorithm; Soft-sensing;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Traditional BP algorithm has the advantages of simple plastic, but there are easy to fall into local extremum, unable to overcome the defects such as slow convergence speed. The particle swarm optimization (PSO) algorithm has the advantages of short training time, small relative error and high control precision. Therefore, this paper designs an modified particle swam optimization algorithm to optimize the BP neural network method, for measuring oil-water interface problem in the process of dehydration of crude oil production, related soft-sensing model is established and simulated experiment, verify the correctness of the model.
引用
收藏
页码：371 / 375
页数：5
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