Improved Particle Swarm Optimization-Based BP Neural Networks for Aero-Optical Imaging Deviation Prediction
In this article, an improved particle swarm optimization (IPSO) algorithm based on similarity and random mutation is raised. The diversity of particles in the population is decided by the size of the aggregation. When the aggregation degree of particles in the population surpass a certain threshold,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9508120/ |
| _version_ | 1818996301539311616 |
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| author | Liang Xu Ziye Zhang Yuan Yao Zhenhua Yu |
| author_facet | Liang Xu Ziye Zhang Yuan Yao Zhenhua Yu |
| author_sort | Liang Xu |
| collection | DOAJ |
| description | In this article, an improved particle swarm optimization (IPSO) algorithm based on similarity and random mutation is raised. The diversity of particles in the population is decided by the size of the aggregation. When the aggregation degree of particles in the population surpass a certain threshold, the concept of similarity is used to measure the similarity between particles and global extremum, and the particles with higher similarity are discretized by mutation strategy. By increasing the particle swarm’s diversity, the population’s local and global search ability tend to balance. The weight and threshold of the back propagation (BP) neural networks are optimized by the IPSO algorithm. Then, the model of the improved particle swarm optimization back propagation neural network (IPSO-BP) is applied to the aero-optical imaging deviation prediction. The results show that the prediction accuracy of the IPSO-BP model is superior to the PSO-BP model, the extreme learning machine (ELM) model, and the least square support vector machine (LSSVM) model, and its convergence speed is faster than that of the PSO-BP neural network model. Finally, the application of deep learning in aero-optical imaging deviation prediction is analyzed compared with the IPSO-BP neural network model. |
| first_indexed | 2024-12-20T21:27:34Z |
| format | Article |
| id | doaj.art-b917e279eac947f09555b0c19f4cda29 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T21:27:34Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b917e279eac947f09555b0c19f4cda292022-12-21T19:26:08ZengIEEEIEEE Access2169-35362022-01-0110267692677710.1109/ACCESS.2021.31026699508120Improved Particle Swarm Optimization-Based BP Neural Networks for Aero-Optical Imaging Deviation PredictionLiang Xu0https://orcid.org/0000-0003-0059-426XZiye Zhang1Yuan Yao2Zhenhua Yu3https://orcid.org/0000-0002-7204-3654Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory for Control Theory and Applications in Complicated Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory for Control Theory and Applications in Complicated Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, ChinaInstitute of Systems Security and Control, College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, ChinaIn this article, an improved particle swarm optimization (IPSO) algorithm based on similarity and random mutation is raised. The diversity of particles in the population is decided by the size of the aggregation. When the aggregation degree of particles in the population surpass a certain threshold, the concept of similarity is used to measure the similarity between particles and global extremum, and the particles with higher similarity are discretized by mutation strategy. By increasing the particle swarm’s diversity, the population’s local and global search ability tend to balance. The weight and threshold of the back propagation (BP) neural networks are optimized by the IPSO algorithm. Then, the model of the improved particle swarm optimization back propagation neural network (IPSO-BP) is applied to the aero-optical imaging deviation prediction. The results show that the prediction accuracy of the IPSO-BP model is superior to the PSO-BP model, the extreme learning machine (ELM) model, and the least square support vector machine (LSSVM) model, and its convergence speed is faster than that of the PSO-BP neural network model. Finally, the application of deep learning in aero-optical imaging deviation prediction is analyzed compared with the IPSO-BP neural network model.https://ieeexplore.ieee.org/document/9508120/Back propagation neural networksimaging deviationimproving particle swarm optimization algorithmprediction |
| spellingShingle | Liang Xu Ziye Zhang Yuan Yao Zhenhua Yu Improved Particle Swarm Optimization-Based BP Neural Networks for Aero-Optical Imaging Deviation Prediction IEEE Access Back propagation neural networks imaging deviation improving particle swarm optimization algorithm prediction |
| title | Improved Particle Swarm Optimization-Based BP Neural Networks for Aero-Optical Imaging Deviation Prediction |
| title_full | Improved Particle Swarm Optimization-Based BP Neural Networks for Aero-Optical Imaging Deviation Prediction |
| title_fullStr | Improved Particle Swarm Optimization-Based BP Neural Networks for Aero-Optical Imaging Deviation Prediction |
| title_full_unstemmed | Improved Particle Swarm Optimization-Based BP Neural Networks for Aero-Optical Imaging Deviation Prediction |
| title_short | Improved Particle Swarm Optimization-Based BP Neural Networks for Aero-Optical Imaging Deviation Prediction |
| title_sort | improved particle swarm optimization based bp neural networks for aero optical imaging deviation prediction |
| topic | Back propagation neural networks imaging deviation improving particle swarm optimization algorithm prediction |
| url | https://ieeexplore.ieee.org/document/9508120/ |
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