[1]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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Temperature-sectioned prediction model for microwave heating process based on adaptive network-based fuzzy inference system

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 1 Page number: 61-69 Column: 学术论文—智能系统 Public date: 2016-02-25
Title:
Temperature-sectioned prediction model for microwave heating process based on adaptive network-based fuzzy inference system
Author(s):
WANG Wei1; 2;  ZHOU Xinzhi1; 2
1. College of Electronic Information, Sichuan University, Chengdu 610065, China;
2. Institute of Intelligent Control, Sichuan University, Chengdu 610064, China
Keywords:
microwave heating process; sectioned temperature prediction; K-means clustering; adaptive Neuro-Fuzzy inference system; BP nerve network; subtraction clustering
CLC:
TP18;TP301.6
DOI:
10.11992/tis.201501028
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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