WU Jin(吴进),GAO Yaqiong,YANG Ling,ZHAO Bo.[J].高技术通讯(英文),2023,29(1):68~77 |
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Boiler flame detection algorithm based on PSO-RBF network |
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DOI:10. 3772/ j. issn. 1006-6748. 2023. 01. 008 |
中文关键词: |
英文关键词: radial basis function(RBF), particle swarm optimization(PSO), flame detection |
基金项目: |
Author Name | Affiliation | WU Jin(吴进) | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) | GAO Yaqiong | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) | YANG Ling | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) | ZHAO Bo | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) |
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中文摘要: |
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英文摘要: |
As the main production tool in the industrial environment, large boilers play a vital role in the conversion and utilization of energy. Therefore, the furnace flame detection technology for boilers has always been a hot issue in the field of industrial automation and intelligence. In order to further improve the timeliness and accuracy of the flame detection network, a radial basis function (RBF) flame detection network based on particle swarm optimization (PSO) algorithm is proposed. First, the proposed algorithm initializes the speed and position parameters of the particles. Then, the parameters of the particles are mapped to the RBF flame detection network. Finally, the algorithm is iteratively updated to obtain the global optimal solution. The PSO-RBF flame detection algorithm adopts a flame sample collection method similar to back propagation (BP) flame detection algorithm, and further improves the collection efficiency. The experimental results show that the PSO-RBF flame detection network has good accuracy and faster convergence speed in the given data samples. In the flame data samples, the detection accuracy of the PSO-RBF flame detection algorithm reaches 90.5%. |
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