FENG Hongwei, LIU Yuanyuan, WEN Ziteng, TAN Yong. Recognition Algorithm for an Infrared Flame Detector Based on an Improved Takagi-Sugeno Fuzzy Radial Basis Function Neural Network[J]. Infrared Technology , 2021, 43(1): 37-43.
Citation: FENG Hongwei, LIU Yuanyuan, WEN Ziteng, TAN Yong. Recognition Algorithm for an Infrared Flame Detector Based on an Improved Takagi-Sugeno Fuzzy Radial Basis Function Neural Network[J]. Infrared Technology , 2021, 43(1): 37-43.

Recognition Algorithm for an Infrared Flame Detector Based on an Improved Takagi-Sugeno Fuzzy Radial Basis Function Neural Network

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  • Received Date: April 18, 2020
  • Revised Date: December 17, 2020
  • To address the data loss, distortion, and saturation of a single non-flame channel that may occur in a three-band infrared flame detector, a robust fusion algorithm for flame recognition based on a radial basis function (RBF) neural network entailing an improved Takagi-Sugeno (T-S) model is proposed in this paper. In this algorithm, the number of fuzzy rules required by the model is determined by a clustering algorithm. The membership degree of the feature component is added to the subsequent fuzzy polynomial to generate node output, and the weighted fuzzy node activation degree and feature characterization coefficient are defined to replace the Markov distance (fuzzy rule applicability) of the original model. Through the design of a three-band flame detector and routine and robustness experiments, it is shown that the proposed model significantly improves the number of nodes, convergence speed, accuracy, generalization ability, and robustness as compared with those of the traditional T-S model RBF neural network and genetic algorithm-back propagation models.
  • [1]
    魏崇毓, 王馨民.基于红外、紫外双波段探测的智能水炮系统设计[J].红外技术, 2016, 38(10): 877-883. DOI: 10.11846/j.issn.1001_8891.201610012

    WEI Chongyu, WANG Xinmin. Design of Intelligent Water Cannon System Based on PIRS and UV Detection[J]. Infrared Technology, 2016, 38(10): 877-883. DOI: 10.11846/j.issn.1001_8891.201610012
    [2]
    邓理文, 刘晓军.基于模糊神经网络的智能火灾探测方法研究[J]. 消防科学与技术, 2019, 38(4): 522-525. DOI: 10.3969/j.issn.1009-0029.2019.04.021

    DENG Liwen, LIU Xiaojun. Intelligent fire detection method based on fuzzy neural network[J]. Fire Science and Technology, 2019, 38(4): 522-525. DOI: 10.3969/j.issn.1009-0029.2019.04.021
    [3]
    YAO Lina, ZHANG Yanna. Fault diagnosis and model predictive tolerant control for non-Gaussian stochastic distribution control systems based on T-S fuzzy model[J]. International Journal of Control Automation & Systems, 2017, 15(1): 2921-2929.
    [4]
    程玉虎, 王雪松, 孙伟.自适应T-S型模糊径向基函数网络[J]. 系统仿真学报, 2007(19): 4440-4444. DOI: 10.3969/j.issn.1004-731X.2007.19.021

    CHENG Yuhu, WANG Xuesong, SUN Wei. Proposal of Adaptive T-S Fuzzy Radial Basis Function Network[J]. Journal of System Simulation, 2007(19): 4440-4444. DOI: 10.3969/j.issn.1004-731X.2007.19.021
    [5]
    WU Qinghui, WANG Xinjun, SHEN Qinghuan. Research on dynamic modeling and simulation of axial-flow pumping system based on RBF neural network[J]. Neurocomputing, 2016, 186: 200-206. DOI: 10.1016/j.neucom.2015.12.064
    [6]
    张谦, 王双红, 陈海峰.基于RBF模糊神经网络的垂直轴风力机设计[J]. 计算机测量与控制, 2014, 22(7): 2237-2239, 2243. DOI: 10.3969/j.issn.1671-4598.2014.07.072

    ZHANG Qian, WANG Shuanghong, CHEN Haifeng. Research on vertical axis wind turbine control algorithm based on RBF fuzzy neural network[J]. Computer Measurement and Control, 2014, 22(7): 2237-2239, 2243. DOI: 10.3969/j.issn.1671-4598.2014.07.072
    [7]
    JUANG Chiafeng, CHIU Shihhsuan, CHANG Shuwew. A Self- Organizing TS-Type Fuzzy Network With Support Vector Learning and its Application to Classification Problems[J]. IEEE Transactions on Fuzzy Systems, 2007, 15(5): 998-1008. DOI: 10.1109/TFUZZ.2007.894980
    [8]
    张颖.改进的T-S模糊神经网络在化工软测量中的应用[J]. 电子测量与仪器学报, 2010, 24(6): 585-589. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201006017.htm

    ZHANG Ying. Improved T-S fuzzy neural network applied in soft sensing of chemical industry[J]. Journal of Electronic Measurement and Instrument, 2010, 24(6): 585-589. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201006017.htm
    [9]
    阮慧, 党德鹏.基于RBF模糊神经网络的信息安全风险评估[J]. 计算机工程与设计, 2011, 32(6): 2113-2115, 2128. https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201106060.htm

    RUAN Hui, DANG Depeng. Risk assessment of information securitybased on RBF fuzzy neural network[J]. Computer Engineering and Design, 2011, 32(6): 2113-2115, 2128. https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201106060.htm
    [10]
    袁积德.三波段红外火焰探测器的研究与开发[D].杭州: 浙江大学, 2012.

    YUAN Jide. Research and Development of Triple Channels Infrared Flame Detector[D]. Hangzhou: Zhejiang University, 2012.
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