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

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^1,,
刘媛媛^{2, 3},
温子腾³,
谭勇³

1.
无锡职业技术学院，江苏无锡 214121
2.
无锡科技职业学院，江苏无锡 214028
3.
江南大学物联网工程学院，江苏无锡 214122

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
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- 被引次数: 0
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

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

1.
Wuxi Institute of Technology, Wuxi 214122, China
2.
Wuxi Professional College of Science and Technology, Wuxi 214028, China
3.
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China

摘要

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。
- 红外火焰探测器 /
- 改进型T-S /
- RBF神经网络 /
- 识别算法
Abstract: 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.
- infrared flame detector /
- improved T-S /
- RBF neural network /
- recognition algorithm

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图 1 三波段火焰探测器结构框图

Figure 1. Structure diagram of three-band flame detector

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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

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图 3 正庚烷燃烧数据图

Figure 3. Data chart of n-heptane combustion

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图 4 电烙铁采集数据图

Figure 4. Data chart of electric iron collection

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图 5 太阳光采集数据图

Figure 5. Data chart of natural light collection

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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

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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

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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

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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

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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

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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

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图 12 模型归一化训练误差比较图

Figure 12. Comparison of normalized training errors of models

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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

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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

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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%

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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%

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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%

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