A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles
Currently, for years, unmanned aerial vehicles have been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, it can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of colla...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-04-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.1177/17298806231190960 |
| _version_ | 1797208332744785920 |
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| author | ZY Chen Yahui Meng Ruei-Yuan Wang Rong Jiang Timothy Chen |
| author_facet | ZY Chen Yahui Meng Ruei-Yuan Wang Rong Jiang Timothy Chen |
| author_sort | ZY Chen |
| collection | DOAJ |
| description | Currently, for years, unmanned aerial vehicles have been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, it can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of collaborative unmanned aerial vehicles will offer diverse interpretations which support a multiperspective view of the scene. Due to diverse interpretations of unmanned aerial vehicles usually deviates, thus, unmanned aerial vehicles require a consensus interpretation for the scenario. To previous purposes, this study presents an original consensus-based method to pilot multi-unmanned aerial vehicle systems for achieving consensus on their observation as well as constructing a group situation-based depiction of the scenario. Further, a fuzzy neural network generalized prediction control system known as a recurrent self-evolving fuzzy neural network is mainly used to ensure stability through the use of a descending gradient online learning rule. At the same time, users can think along the lines of evolutionary biological design. Unmanned aerial vehicles can be modeled as system experts for solving group problems that require the definition of conditions that best describe the scene. First, this method allows each unmanned aerial vehicle to set high-level conditions for detection events by aggregating events based on fuzzy information. These aggregated events are modeled by a fuzzy system ontology, which allows each unmanned aerial vehicle to report its preferences in conditions. Therefore, the interpretation of each drone is compressed to achieve a collective interpretation of the state. The final polls, consent and affinity polls confirmed the final decision group’s reliability ratings. The rated consensus indicates how well the collective interpretation of the scene matches each drone’s point of view. |
| first_indexed | 2024-04-24T09:37:08Z |
| format | Article |
| id | doaj.art-0d23ecdd9e3649b98bc0ba46ed17db11 |
| institution | Directory Open Access Journal |
| issn | 1729-8814 |
| language | English |
| last_indexed | 2024-04-24T09:37:08Z |
| publishDate | 2024-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj.art-0d23ecdd9e3649b98bc0ba46ed17db112024-04-15T09:05:34ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142024-04-012110.1177/17298806231190960A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehiclesZY Chen0Yahui Meng1Ruei-Yuan Wang2Rong Jiang3Timothy Chen4 School of Science, Guangdong University of Petrochemical Technology, Maoming, Guangdong, People’s Republic of China School of Science, Guangdong University of Petrochemical Technology, Maoming, Guangdong, People’s Republic of China School of Science, Guangdong University of Petrochemical Technology, Maoming, Guangdong, People’s Republic of China School of Science, Guangdong University of Petrochemical Technology, Maoming, Guangdong, People’s Republic of China Division of Engineering and Applied Science, Caltech, CA, USACurrently, for years, unmanned aerial vehicles have been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, it can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of collaborative unmanned aerial vehicles will offer diverse interpretations which support a multiperspective view of the scene. Due to diverse interpretations of unmanned aerial vehicles usually deviates, thus, unmanned aerial vehicles require a consensus interpretation for the scenario. To previous purposes, this study presents an original consensus-based method to pilot multi-unmanned aerial vehicle systems for achieving consensus on their observation as well as constructing a group situation-based depiction of the scenario. Further, a fuzzy neural network generalized prediction control system known as a recurrent self-evolving fuzzy neural network is mainly used to ensure stability through the use of a descending gradient online learning rule. At the same time, users can think along the lines of evolutionary biological design. Unmanned aerial vehicles can be modeled as system experts for solving group problems that require the definition of conditions that best describe the scene. First, this method allows each unmanned aerial vehicle to set high-level conditions for detection events by aggregating events based on fuzzy information. These aggregated events are modeled by a fuzzy system ontology, which allows each unmanned aerial vehicle to report its preferences in conditions. Therefore, the interpretation of each drone is compressed to achieve a collective interpretation of the state. The final polls, consent and affinity polls confirmed the final decision group’s reliability ratings. The rated consensus indicates how well the collective interpretation of the scene matches each drone’s point of view.https://doi.org/10.1177/17298806231190960 |
| spellingShingle | ZY Chen Yahui Meng Ruei-Yuan Wang Rong Jiang Timothy Chen A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles International Journal of Advanced Robotic Systems |
| title | A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles |
| title_full | A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles |
| title_fullStr | A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles |
| title_full_unstemmed | A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles |
| title_short | A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles |
| title_sort | novel recurrent self evolving fuzzy neural network for consensus decision making of unmanned aerial vehicles |
| url | https://doi.org/10.1177/17298806231190960 |
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