TEMPERATURE COMPENSATION STRATEGY OF PRESSURE SENSOR BASED ON BP NEURAL NETWORK OPTIMIZED BY GLOWWORM SWARM OPTIMIZATION
In order to solve the problem that temperature drift of silicon piezoresistive pressure sensor affects the accuracy of engineering measurement,proposed a temperature compensation strategy for BP neural network based on glowworm swarm optimization. The generalized BP neural network is used to optimiz...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2020-01-01
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| Series: | Jixie qiangdu |
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| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.01.017 |
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| author | WANG Hui FU Peng SONG YuNing |
| author_facet | WANG Hui FU Peng SONG YuNing |
| author_sort | WANG Hui |
| collection | DOAJ |
| description | In order to solve the problem that temperature drift of silicon piezoresistive pressure sensor affects the accuracy of engineering measurement,proposed a temperature compensation strategy for BP neural network based on glowworm swarm optimization. The generalized BP neural network is used to optimize the weights and thresholds by using the firefly algorithm,thus improving the generalization performance and searching speed of the neural network,carried out temperature compensation of pressure sensor by optimized BP neural network. The temperature compensation performance of optimized BP neural network compares to that of conventional neural network and particle swarm optimization neural network. The results showed that compared with the conventional neural network and PSO optimization BP neural network,the optimized GSO optimization BP neural network is effective.The compensation error of GSO-BP neural network is 52% less than that of BP and 23% less than that of PSO-BP.Considering the time of compensation,the comprehensive performance of the BP neural network optimized by GSO is better.The compensated sensor data meet the experimental requirements of the subject.The compensation algorithm is feasible. |
| format | Article |
| id | doaj-art-72ede1644df243dbb46c0dcb1953179c |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-72ede1644df243dbb46c0dcb1953179c2025-01-15T02:28:47ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692020-01-014210911430607251TEMPERATURE COMPENSATION STRATEGY OF PRESSURE SENSOR BASED ON BP NEURAL NETWORK OPTIMIZED BY GLOWWORM SWARM OPTIMIZATIONWANG HuiFU PengSONG YuNingIn order to solve the problem that temperature drift of silicon piezoresistive pressure sensor affects the accuracy of engineering measurement,proposed a temperature compensation strategy for BP neural network based on glowworm swarm optimization. The generalized BP neural network is used to optimize the weights and thresholds by using the firefly algorithm,thus improving the generalization performance and searching speed of the neural network,carried out temperature compensation of pressure sensor by optimized BP neural network. The temperature compensation performance of optimized BP neural network compares to that of conventional neural network and particle swarm optimization neural network. The results showed that compared with the conventional neural network and PSO optimization BP neural network,the optimized GSO optimization BP neural network is effective.The compensation error of GSO-BP neural network is 52% less than that of BP and 23% less than that of PSO-BP.Considering the time of compensation,the comprehensive performance of the BP neural network optimized by GSO is better.The compensated sensor data meet the experimental requirements of the subject.The compensation algorithm is feasible.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.01.017Glowworm swarm optimizationBP neural networkSensorTemperature compensation |
| spellingShingle | WANG Hui FU Peng SONG YuNing TEMPERATURE COMPENSATION STRATEGY OF PRESSURE SENSOR BASED ON BP NEURAL NETWORK OPTIMIZED BY GLOWWORM SWARM OPTIMIZATION Jixie qiangdu Glowworm swarm optimization BP neural network Sensor Temperature compensation |
| title | TEMPERATURE COMPENSATION STRATEGY OF PRESSURE SENSOR BASED ON BP NEURAL NETWORK OPTIMIZED BY GLOWWORM SWARM OPTIMIZATION |
| title_full | TEMPERATURE COMPENSATION STRATEGY OF PRESSURE SENSOR BASED ON BP NEURAL NETWORK OPTIMIZED BY GLOWWORM SWARM OPTIMIZATION |
| title_fullStr | TEMPERATURE COMPENSATION STRATEGY OF PRESSURE SENSOR BASED ON BP NEURAL NETWORK OPTIMIZED BY GLOWWORM SWARM OPTIMIZATION |
| title_full_unstemmed | TEMPERATURE COMPENSATION STRATEGY OF PRESSURE SENSOR BASED ON BP NEURAL NETWORK OPTIMIZED BY GLOWWORM SWARM OPTIMIZATION |
| title_short | TEMPERATURE COMPENSATION STRATEGY OF PRESSURE SENSOR BASED ON BP NEURAL NETWORK OPTIMIZED BY GLOWWORM SWARM OPTIMIZATION |
| title_sort | temperature compensation strategy of pressure sensor based on bp neural network optimized by glowworm swarm optimization |
| topic | Glowworm swarm optimization BP neural network Sensor Temperature compensation |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.01.017 |
| work_keys_str_mv | AT wanghui temperaturecompensationstrategyofpressuresensorbasedonbpneuralnetworkoptimizedbyglowwormswarmoptimization AT fupeng temperaturecompensationstrategyofpressuresensorbasedonbpneuralnetworkoptimizedbyglowwormswarmoptimization AT songyuning temperaturecompensationstrategyofpressuresensorbasedonbpneuralnetworkoptimizedbyglowwormswarmoptimization |