Intelligent detecting system based on mixture of expert networks

ZHANG Dong-zhi; XIA Bo-kai; REN Dong-yan

doi:10.13873/j.1000-97872007.06.016

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Intelligent detecting system based on mixture of expert networks

更新时间：2025-09-11

- Intelligent detecting system based on mixture of expert networks
- Issue 6, Pages: 77-79+93(2007)
- 作者机构：
  
  1. 中国石油大学(华东)信息与控制工程学院
  2. 中国石油大学（华东）信息与控制工程学院,山东,东营,257061
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872007.06.016
  CLC： TP274.4
- Published：2007
- 稿件说明：
移动端阅览
[1]张冬至,夏伯锴,任冬艳.基于混合专家网络的智能检测系统[J].传感器与微系统,2007(06):77-79+93.

ZHANG Dong-zhi, XIA Bo-kai, REN Dong-yan. Intelligent detecting system based on mixture of expert networks[J]. 2007, (6): 77-79+93.
[1]张冬至,夏伯锴,任冬艳.基于混合专家网络的智能检测系统[J].传感器与微系统,2007(06):77-79+93. DOI： 10.13873/j.1000-97872007.06.016.

ZHANG Dong-zhi, XIA Bo-kai, REN Dong-yan. Intelligent detecting system based on mixture of expert networks[J]. 2007, (6): 77-79+93. DOI： 10.13873/j.1000-97872007.06.016.

摘要

为解决原油含水率测量中存在的问题

提出一种基于多传感器技术的智能检测系统

对原油的水分、温度、水矿化度进行检测

并采用混合专家网络对多传感器信息进行智能融合处理

以提高含水率的检测精度。在提出的混合专家网络中

采用了专家决策、多神经网络集成等先进技术

且专家子网络采用LM算法和最优停止法相结合以确定网络最佳权值

提高模型的泛化能力。仿真结果表明:该混合专家网络模型在原油含水率测量系统中取得很好的应用效果

获得较高的测量精度。

Abstract

Based on multi-sensor technology

an intelligent detecting system is proposed to solve problems in the measurement of water content ratio in crude oil.In this system

water content

temperature and water salinity in crude oil are detected

and then multi-sensor information fusion technology based on mixture of expert networks is used to improve detecting precision of water content.Some advanced technologies

such as expert decision and multiple neural network integration

are introduced in the mixture expert networks.Furthermore

LM arithmetic and early stopping method are integrated in the expert sub-network for choosing optimization weights to improve generalization ability of network model.The simulation results show that the model of the mixture expert networks can be used well in the intelligent detecting system for water content ratio in crude oil

and higher measurement precision is acquired.

关键词

Keywords

references

原油含水率测量技术及其进展 [J]. 张乃禄,薛朝妹,徐竟天,张家田. 石油工业技术监督 . 2005(11)

混合专家网络的锅炉故障诊断样本及误差分析 [J]. 吕佳,熊浩. 重庆师范大学学报(自然科学版) . 2004(02)

神经网络学习算法的过拟合问题及解决方法 [J]. 李俭川,秦国军,温熙森,胡茑庆. 振动、测试与诊断 . 2002(04)

储罐原油含水率在线测量及动态油量计量技术的研究 [J]. 陈祥光,薛锦诚,傅若农. 仪器仪表学报 . 2000(06)

Improved learning algorithms for mixture of experts in multiclass classification [J] . K. Chen,L. Xu,H. Chi. Neural Networks . 1999 (9)

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Related Institution

中国石油大学信息与控制工程学院

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