Multi-source data recognition and fusion algorithm based on a two-layer genetic algorithm–back propagation model
Traditional rainfall data collection mainly relies on rain buckets and meteorological data. It rarely considers the impact of sensor faults on measurement accuracy. To solve this problem, a two-layer genetic algorithm–backpropagation (GA-BP) model is proposed. The algorithm focuses on multi-source d...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2024.1520605/full |
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| author | Zhuang Xiong Zhuang Xiong Jun Ma Bohang Chen Haiming Lan Yong Niu |
| author_facet | Zhuang Xiong Zhuang Xiong Jun Ma Bohang Chen Haiming Lan Yong Niu |
| author_sort | Zhuang Xiong |
| collection | DOAJ |
| description | Traditional rainfall data collection mainly relies on rain buckets and meteorological data. It rarely considers the impact of sensor faults on measurement accuracy. To solve this problem, a two-layer genetic algorithm–backpropagation (GA-BP) model is proposed. The algorithm focuses on multi-source data identification and fusion. Rainfall data from a sensor array are first used. The GA optimizes the weights and thresholds of the BP neural network. It determines the optimal population and minimizes fitness values. This process builds a GA-BP model for recognizing sensor faults. A second GA-BP network is then created based on fault data. This model achieves data fusion output. The two-layer GA-BP algorithm is compared with a single BP neural network and actual expected values to test its performance. The results show that the two-layer GA-BP algorithm reduces data fusion runtime by 2.37 s compared to the single-layer BP model. For faults such as lost signals, high-value bias, and low-value bias, recognition accuracies improve by 26.09%, 18.18%, and 7.15%, respectively. The mean squared error is 3.49 mm lower than that of the single-layer BP model. The fusion output waveform is also smoother with less fluctuation. These results confirm that the two-layer GA-BP model improves system robustness and generalization. |
| format | Article |
| id | doaj-art-d30e750b02184b559715ff4a3c442008 |
| institution | Kabale University |
| issn | 2624-909X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj-art-d30e750b02184b559715ff4a3c4420082025-01-13T06:11:04ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2025-01-01710.3389/fdata.2024.15206051520605Multi-source data recognition and fusion algorithm based on a two-layer genetic algorithm–back propagation modelZhuang Xiong0Zhuang Xiong1Jun Ma2Bohang Chen3Haiming Lan4Yong Niu5The College of Computer, Qinghai Normal University, Xining, ChinaDepartment of Mechanical and Electrical Engineering, College of Xining Urban Vocational and Technical, Xining, ChinaThe College of Computer, Qinghai Normal University, Xining, ChinaThe College of Computer, Qinghai Normal University, Xining, ChinaThe College of Computer, Qinghai Normal University, Xining, ChinaThe College of Computer, Qinghai Normal University, Xining, ChinaTraditional rainfall data collection mainly relies on rain buckets and meteorological data. It rarely considers the impact of sensor faults on measurement accuracy. To solve this problem, a two-layer genetic algorithm–backpropagation (GA-BP) model is proposed. The algorithm focuses on multi-source data identification and fusion. Rainfall data from a sensor array are first used. The GA optimizes the weights and thresholds of the BP neural network. It determines the optimal population and minimizes fitness values. This process builds a GA-BP model for recognizing sensor faults. A second GA-BP network is then created based on fault data. This model achieves data fusion output. The two-layer GA-BP algorithm is compared with a single BP neural network and actual expected values to test its performance. The results show that the two-layer GA-BP algorithm reduces data fusion runtime by 2.37 s compared to the single-layer BP model. For faults such as lost signals, high-value bias, and low-value bias, recognition accuracies improve by 26.09%, 18.18%, and 7.15%, respectively. The mean squared error is 3.49 mm lower than that of the single-layer BP model. The fusion output waveform is also smoother with less fluctuation. These results confirm that the two-layer GA-BP model improves system robustness and generalization.https://www.frontiersin.org/articles/10.3389/fdata.2024.1520605/fullmulti-source data fusionBP neural networklegacy algorithmgenetic algorithm–optimized back propagation networkmulti-sensor fault recognition |
| spellingShingle | Zhuang Xiong Zhuang Xiong Jun Ma Bohang Chen Haiming Lan Yong Niu Multi-source data recognition and fusion algorithm based on a two-layer genetic algorithm–back propagation model Frontiers in Big Data multi-source data fusion BP neural network legacy algorithm genetic algorithm–optimized back propagation network multi-sensor fault recognition |
| title | Multi-source data recognition and fusion algorithm based on a two-layer genetic algorithm–back propagation model |
| title_full | Multi-source data recognition and fusion algorithm based on a two-layer genetic algorithm–back propagation model |
| title_fullStr | Multi-source data recognition and fusion algorithm based on a two-layer genetic algorithm–back propagation model |
| title_full_unstemmed | Multi-source data recognition and fusion algorithm based on a two-layer genetic algorithm–back propagation model |
| title_short | Multi-source data recognition and fusion algorithm based on a two-layer genetic algorithm–back propagation model |
| title_sort | multi source data recognition and fusion algorithm based on a two layer genetic algorithm back propagation model |
| topic | multi-source data fusion BP neural network legacy algorithm genetic algorithm–optimized back propagation network multi-sensor fault recognition |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2024.1520605/full |
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