[1]王伟,周新志.ANFIS微波加热过程分段温度预测模型[J].智能系统学报编辑部,2016,11(1):61-69.[doi:10.11992/tis.201501028]
 WANG Wei,ZHOU Xinzhi.Temperature-sectioned prediction model for microwave heating process based on adaptive network-based fuzzy inference system[J].CAAI Transactions on Intelligent Systems,2016,11(1):61-69.[doi:10.11992/tis.201501028]
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ANFIS微波加热过程分段温度预测模型(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年1期
页码:
61-69
栏目:
出版日期:
2016-02-25

文章信息/Info

Title:
Temperature-sectioned prediction model for microwave heating process based on adaptive network-based fuzzy inference system
作者:
王伟12 周新志12
1. 四川大学电子信息学院, 四川成都 610065;
2. 四川大学智能控制研究所, 四川成都 610064
Author(s):
WANG Wei12 ZHOU Xinzhi12
1. College of Electronic Information, Sichuan University, Chengdu 610065, China;
2. Institute of Intelligent Control, Sichuan University, Chengdu 610064, China
关键词:
微波加热过程分段温度预测K均值聚类ANFISBP神经网络减法聚类
Keywords:
microwave heating processsectioned temperature predictionK-means clusteringadaptive Neuro-Fuzzy inference systemBP nerve networksubtraction clustering
分类号:
TP18;TP301.6
DOI:
10.11992/tis.201501028
摘要:
在微波加热过程中加热介质在不同温度阶段有不同的内部特性,传统的温度预测方法难于同时对加热介质低温段与高温段温度取得满意的预测结果。为此提出了一种基于ANFIS 的分段温度预测模型,该方法建立基于K均值聚类法的温度划分机制,并采用不同结构的ANFIS预测加热介质不同温度阶段的温度。低温阶段构建常规ANFIS预测温度,高温阶段利用减法聚类能从数据中确定模糊规则的特性构建ANFIS预测温度。仿真结果表明,与采用单一结构的ANFIS和BP(back propagation)神经网络的预测结果相比,ANFIS分段温度预测模型可同时在加热介质低温段与高温段取得较好的预测结果,模型效率可达到97.41%,显著提高了预测准确率,这有助于提高实际微波加热过程的生产效率和安全性。
Abstract:
During the microwave heating process, materials in different temperature regions have different internal characteristics. Using traditional temperature forecasting methods, it is difficult to obtain satisfactory prediction results for both low-and high-temperature sections in a medium. To solve this problem, this study proposes a new temperature-sectioned forecasting model based on the ANFIS (adaptive neuro-fuzzy inference system). For this method, we established a temperature-division mechanism based on K-means clustering. Additionally, we used an ANFIS with different structures to forecast the temperature of the heated medium at different stages. We also constructed a conventional ANFIS to predict a material’s low temperature and a subtraction-clustering ANFIS that determines the fuzzy rules from data to predict a material’s high temperature. Simulation results demonstrate that the proposed method achieves satisfactory results for both low-and high-temperature sections when compared to ANFISs and BP(back propagation) networks with a single structure. Model efficiency can reach 97.41% and the prediction accuracy is significantly improved. The proposed model can improve the efficiency and safety of the microwave heating process.

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备注/Memo

备注/Memo:
收稿日期:2015-01-30;改回日期:。
基金项目:国家"973"计划资助项目(2013CB328903).
作者简介:王伟,男,1989年生,硕士研究生,主要研究方向为智能控制;周新志,男,1966年生,教授,博士,主要研究方向为人工智能、智能控制技术及应用。作为主要研究者或项目负责人承担了国家"973"计划、国家自然科学基金项目、四川省科技攻关项目等多项,获国家专利2项,发表学术论文30余篇。
通讯作者:周新志.E-mail:xz.zhou@scu.edu.cn.
更新日期/Last Update: 1900-01-01