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基于改进型T-S模糊RBF神经网络的红外火焰探测器识别算法

冯宏伟 刘媛媛 温子腾 谭勇

冯宏伟, 刘媛媛, 温子腾, 谭勇. 基于改进型T-S模糊RBF神经网络的红外火焰探测器识别算法[J]. 红外技术, 2021, 43(1): 37-43.
引用本文: 冯宏伟, 刘媛媛, 温子腾, 谭勇. 基于改进型T-S模糊RBF神经网络的红外火焰探测器识别算法[J]. 红外技术, 2021, 43(1): 37-43.
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.

基于改进型T-S模糊RBF神经网络的红外火焰探测器识别算法

基金项目: 

国家自然科学基金项目 61374047

详细信息
    作者简介:

    冯宏伟(1982-),男,山东郓城,硕士,副教授/高工,研究方向为智能仪器仪表的研发与设计。E-mail: fenghw@wxit.edu.cn

  • 中图分类号: TN215

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

  • 摘要: 针对三波段红外火焰探测器中可能出现的单一非火焰波段通道的数据丢失、失真、饱和3种对火焰特征数据的强干扰情况,本文提出了一种改进型T-S(Takagi-Sugeno,高木-关野)模型RBF(Radial Basis Function,径向基函数)神经网络的火焰识别的鲁棒性融合算法。该算法通过聚类算法确定模型需要的模糊规则数,在模糊后件多项式中加入特征分量隶属度生成节点输出,同时定义了加权模糊节点激活度和特征表征系数代替了原先模型的马氏距离(模糊规则适用度)。通过设计三波段火焰探测器并进行了常规及鲁棒性实验,实验数据证实,改进型模型在隐含层所需节点数、收敛速度、精度、泛化能力、鲁棒性上较传统T-S模型的RBF神经网络模型、GA(Genetic Algorithm,遗传算法)-BP(Back Propagation,反向传播)模型都有明显的提升。
  • 图  1  三波段火焰探测器结构框图

    Figure  1.  Structure diagram of three-band flame detector

    图  2  改进型融合T-S模型的RBF模糊神经网络结构图

    Figure  2.  Structural diagram of RBF fuzzy neural network based on improved T-S model

    图  3  正庚烷燃烧数据图

    Figure  3.  Data chart of n-heptane combustion

    图  4  电烙铁采集数据图

    Figure  4.  Data chart of electric iron collection

    图  5  太阳光采集数据图

    Figure  5.  Data chart of natural light collection

    图  6  正庚烷火焰下3.8 μm通道数据丢失图

    Figure  6.  Data loss of 3.8 μm channel in N-heptane flame

    图  7  正庚烷火焰下5.0 μm通道数据丢失图

    Figure  7.  Data loss of 5.0 μm channel in N-heptane flame

    图  8  正庚烷火焰下3.8 μm通道数据失真图

    Figure  8.  Data distortion of 3.8 μm channel in N-heptane flame

    图  9  正庚烷火焰下5.0 μm通道数据丢失图

    Figure  9.  Data distortion of 5.0 μm channel in N-heptane flame

    图  10  正庚烷火焰下3.8 μm通道数据饱和图

    Figure  10.  Data saturation of 3.8 μm in N-heptane flame

    图  11  正庚烷火焰下5.0 μm通道数据饱和图

    Figure  11.  Data saturation of 5.0 μm in N-heptane flame

    图  12  模型归一化训练误差比较图

    Figure  12.  Comparison of normalized training errors of models

    表  1  部分样本示意表

    Table  1.   Schematic table of some samples

    Sample No. x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12
    1 2.58 2.51 2.46 1.02 0.97 16 0.840 0.032 0.023 0.027 1.6875 0.207
    2 2.39 2.71 2.43 0.88 0.89 12 0.408 0.032 0.020 0.023 2.25 0.284
    3 2.07 2.32 2.10 0.89 0.90 40 0.237 0.017 0.020 0.012 1.6875 0.061
    4 2.15 2.24 2.29 0.95 1.02 4 0.306 0.0261 0.023 0.019 0.5625 0.062
    下载: 导出CSV

    表  2  网络效果比较

    Table  2.   Comparison of network effects

    Network type RSME training Training accuracy RSME Test Test accuracy Node
    Improved T-S-RBF 0.007 100% 0.006 100% 15
    Traditional T-S-RBF 0.025 100% 0.071 97.5% 50
    GA-BP 0.035 100% 0.074 98.7% 17
    下载: 导出CSV

    表  3  数据丢失模型效果比价

    Table  3.   Comparison of data loss models

    Network type RSME of 3.8 μm Test accuracy RSME of 5.0 μm Test accuracy
    Improved T-S-RBF 0.0226 100% 0.0051 100%
    Traditional T-S-RBF 0.6185 90% 0.9356 77%
    GA-BP 1.3362 53% 0.2379 99%
    下载: 导出CSV

    表  4  数据失真模型效果比价

    Table  4.   Comparison of data distortion models

    Network type RSME of 3.8 μm Test accuracy RSME of 5.0 μm Test accuracy
    Improved T-S-RBF 0.0093 100% 0.0109 100%
    Traditional T-S-RBF 0.386 96% 0.6542 86%
    GA-BP 0.3769 94% 0.0605 100%
    下载: 导出CSV

    表  5  数据饱和模型效果比价

    Table  5.   Comparison of data saturation models

    Network type RSME of 3.8 μm Test accuracy RSME of 5.0 μm Test accuracy
    Improved T-S-RBF 0.0227 100% 0.0475 100%
    Traditional T-S-RBF 1.4715 45% 1.2798 51%
    GA-BP 0.6012 89% 0.6364 86%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-19
  • 修回日期:  2020-12-18
  • 刊出日期:  2021-01-20

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