Soil Salinization Level Monitoring and Classifying by Mixed Chaotic Systems
Soil salinization process is a complex non-linear dynamic evolution. To classify a system with this type of non-linear characteristic, this study proposed a mixed master/slave chaotic system based on Chua’s circuit and a fractional-order Chen-Lee chaotic system to classify soil salinization level. T...
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/19/3819 |
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| author | Anhong Tian Chengbiao Fu Her-Terng Yau Xiao-Yi Su Heigang Xiong |
| author_facet | Anhong Tian Chengbiao Fu Her-Terng Yau Xiao-Yi Su Heigang Xiong |
| author_sort | Anhong Tian |
| collection | DOAJ |
| description | Soil salinization process is a complex non-linear dynamic evolution. To classify a system with this type of non-linear characteristic, this study proposed a mixed master/slave chaotic system based on Chua’s circuit and a fractional-order Chen-Lee chaotic system to classify soil salinization level. The subject is the soil in Xinjiang with different levels of human interference. A fractional-order Chen-Lee chaotic system was constructed, and the spectral signal processed by the Chua’s non-linear circuit was substituted into the master/slave chaotic system. The chaotic dynamic errors with different fractional orders were calculated. The comparative analysis showed that 0.1-order has the largest chaotic dynamic error change, which produced two distinct and divergent results. Thus, this study converted the chaotic dynamic errors of fractional 0.1-order into chaotic attractors to build an extension matter-element model. Finally, we compared the soil salt contents (SSC) from the laboratory chemical analysis with the results of the extension theory classification. The comparison showed that the combination of fractional order mixed master/slave chaotic system and extension theory has high classification accuracy for soil salinization level. The results of this system match the result of the chemical analysis. The classification accuracy of the calibration set data was 100%, and the classification accuracy of the validation set data was 90%. This method is the first use of the mixed master/slave chaotic system in this field and can satisfy certain soil salinization monitoring needs as well as promote the application of the chaotic system in soil salinization monitoring. |
| first_indexed | 2024-03-10T06:53:33Z |
| format | Article |
| id | doaj.art-ea112e0591fa4e639b1dfb95c9f2ab6a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:53:33Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ea112e0591fa4e639b1dfb95c9f2ab6a2023-11-22T16:41:20ZengMDPI AGRemote Sensing2072-42922021-09-011319381910.3390/rs13193819Soil Salinization Level Monitoring and Classifying by Mixed Chaotic SystemsAnhong Tian0Chengbiao Fu1Her-Terng Yau2Xiao-Yi Su3Heigang Xiong4College of Information Engineering, Qujing Normal University, Qujing 655011, ChinaCollege of Information Engineering, Qujing Normal University, Qujing 655011, ChinaDepartment of Mechanical Engineering, National Chung Cheng University, Chiayi 621301, TaiwanGraduate Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, TaiwanCollege of Applied Arts and Science, Beijing Union University, Beijing 100083, ChinaSoil salinization process is a complex non-linear dynamic evolution. To classify a system with this type of non-linear characteristic, this study proposed a mixed master/slave chaotic system based on Chua’s circuit and a fractional-order Chen-Lee chaotic system to classify soil salinization level. The subject is the soil in Xinjiang with different levels of human interference. A fractional-order Chen-Lee chaotic system was constructed, and the spectral signal processed by the Chua’s non-linear circuit was substituted into the master/slave chaotic system. The chaotic dynamic errors with different fractional orders were calculated. The comparative analysis showed that 0.1-order has the largest chaotic dynamic error change, which produced two distinct and divergent results. Thus, this study converted the chaotic dynamic errors of fractional 0.1-order into chaotic attractors to build an extension matter-element model. Finally, we compared the soil salt contents (SSC) from the laboratory chemical analysis with the results of the extension theory classification. The comparison showed that the combination of fractional order mixed master/slave chaotic system and extension theory has high classification accuracy for soil salinization level. The results of this system match the result of the chemical analysis. The classification accuracy of the calibration set data was 100%, and the classification accuracy of the validation set data was 90%. This method is the first use of the mixed master/slave chaotic system in this field and can satisfy certain soil salinization monitoring needs as well as promote the application of the chaotic system in soil salinization monitoring.https://www.mdpi.com/2072-4292/13/19/3819soil salinization degree classificationChua’s chaotic circuitChen-Lee master/slave chaotic systemmixed fractional-order chaotic systemsoil hyperspectral |
| spellingShingle | Anhong Tian Chengbiao Fu Her-Terng Yau Xiao-Yi Su Heigang Xiong Soil Salinization Level Monitoring and Classifying by Mixed Chaotic Systems Remote Sensing soil salinization degree classification Chua’s chaotic circuit Chen-Lee master/slave chaotic system mixed fractional-order chaotic system soil hyperspectral |
| title | Soil Salinization Level Monitoring and Classifying by Mixed Chaotic Systems |
| title_full | Soil Salinization Level Monitoring and Classifying by Mixed Chaotic Systems |
| title_fullStr | Soil Salinization Level Monitoring and Classifying by Mixed Chaotic Systems |
| title_full_unstemmed | Soil Salinization Level Monitoring and Classifying by Mixed Chaotic Systems |
| title_short | Soil Salinization Level Monitoring and Classifying by Mixed Chaotic Systems |
| title_sort | soil salinization level monitoring and classifying by mixed chaotic systems |
| topic | soil salinization degree classification Chua’s chaotic circuit Chen-Lee master/slave chaotic system mixed fractional-order chaotic system soil hyperspectral |
| url | https://www.mdpi.com/2072-4292/13/19/3819 |
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