Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network
Due to the continuous progress in the field of vehicle hardware, the condition that a vehicle cannot load a complex algorithm no longer exists. At the same time, with the progress in the field of vehicle hardware, a number of studies have reported exponential growth in the actual operation. To solve...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1067927 |
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| _version_ | 1832563844665311232 |
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| author | Jingfeng Yang Nanfeng Zhang Ming Li Yanwei Zheng Li Wang Yong Li Ji Yang Yifei Xiang Lufeng Luo |
| author_facet | Jingfeng Yang Nanfeng Zhang Ming Li Yanwei Zheng Li Wang Yong Li Ji Yang Yifei Xiang Lufeng Luo |
| author_sort | Jingfeng Yang |
| collection | DOAJ |
| description | Due to the continuous progress in the field of vehicle hardware, the condition that a vehicle cannot load a complex algorithm no longer exists. At the same time, with the progress in the field of vehicle hardware, a number of studies have reported exponential growth in the actual operation. To solve the problem for a large number of data transmissions in an actual operation, wireless transmission is proposed for text information (including position information) on the basis of the principles of the maximum entropy probability and the neural network prediction model combined with the optimization of the Huffman encoding algorithm, from the exchange of data to the entire data extraction process. The test results showed that the text-type vehicle information based on a compressed algorithm to optimize the algorithm of data compression and transmission could effectively realize the data compression, achieve a higher compression rate and data transmission integrity, and after decompression guarantee no distortion. Therefore, it is important to improve the efficiency of vehicle information transmission, to ensure the integrity of information, to realize the vehicle monitoring and control, and to grasp the traffic situation in real time. |
| format | Article |
| id | doaj-art-5dd8a669b4784cc3b773f5fa1be0256a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5dd8a669b4784cc3b773f5fa1be0256a2025-02-03T01:12:25ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/10679271067927Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural NetworkJingfeng Yang0Nanfeng Zhang1Ming Li2Yanwei Zheng3Li Wang4Yong Li5Ji Yang6Yifei Xiang7Lufeng Luo8Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, ChinaGuangzhou Customs, Guangzhou 510623, ChinaSouth China Agricultural University, Guangzhou 510642, ChinaGuangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, ChinaSchool of Computer Software in Tianjin University, Tianjin 300072, ChinaGuangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, ChinaGuangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, ChinaNorth China Electric Power University, Beijing 102206, ChinaCollege of Mechanical and Electrical Engineering, Foshan University, Foshan 528000, ChinaDue to the continuous progress in the field of vehicle hardware, the condition that a vehicle cannot load a complex algorithm no longer exists. At the same time, with the progress in the field of vehicle hardware, a number of studies have reported exponential growth in the actual operation. To solve the problem for a large number of data transmissions in an actual operation, wireless transmission is proposed for text information (including position information) on the basis of the principles of the maximum entropy probability and the neural network prediction model combined with the optimization of the Huffman encoding algorithm, from the exchange of data to the entire data extraction process. The test results showed that the text-type vehicle information based on a compressed algorithm to optimize the algorithm of data compression and transmission could effectively realize the data compression, achieve a higher compression rate and data transmission integrity, and after decompression guarantee no distortion. Therefore, it is important to improve the efficiency of vehicle information transmission, to ensure the integrity of information, to realize the vehicle monitoring and control, and to grasp the traffic situation in real time.http://dx.doi.org/10.1155/2018/1067927 |
| spellingShingle | Jingfeng Yang Nanfeng Zhang Ming Li Yanwei Zheng Li Wang Yong Li Ji Yang Yifei Xiang Lufeng Luo Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network Complexity |
| title | Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network |
| title_full | Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network |
| title_fullStr | Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network |
| title_full_unstemmed | Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network |
| title_short | Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network |
| title_sort | vehicle information influence degree screening method based on gep optimized rbf neural network |
| url | http://dx.doi.org/10.1155/2018/1067927 |
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