Adaptive memetic differential evolution-back propagation-fuzzy neural network algorithm for robot control
•A novel intelligent control approach AMDE-BP-FNN to control complex robot system.•There is layer upon layer of optimal structure for AMDE-BP-FNN.•Making the best of the advantages of AMDE algorithm and BP algorithm, to optimize FNN.•The superiority of AMDE-BP-FNN is highlighted by quantitative comp...
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| Vydané v: | Information sciences Ročník 637; s. 118940 |
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| Jazyk: | English |
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01.08.2023
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | •A novel intelligent control approach AMDE-BP-FNN to control complex robot system.•There is layer upon layer of optimal structure for AMDE-BP-FNN.•Making the best of the advantages of AMDE algorithm and BP algorithm, to optimize FNN.•The superiority of AMDE-BP-FNN is highlighted by quantitative comparison.
This study established an adaptive memetic differential evolution-back propagation-fuzzy neural network (AMDE-BP-FNN) control method to achieve high-efficiency and precise control of robots with complex dynamic characteristics while reducing control costs. The adaptive differential evolution (ADE) method was applied to search the optimal parameters in the global scope and delimited the pseudo-global search scope. The memetic differential evolution (MDE) method was used to search for optimal parameters in the pseudo-global scope, and the probability factor was set to decide whether to use the back propagation (BP) algorithm for online optimization. Finally, simulations, experiments, and real-world applications were conducted. The results indicated the high efficiency, high precision, and viability of the proposed AMDE-BP-FNN method. |
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| AbstractList | •A novel intelligent control approach AMDE-BP-FNN to control complex robot system.•There is layer upon layer of optimal structure for AMDE-BP-FNN.•Making the best of the advantages of AMDE algorithm and BP algorithm, to optimize FNN.•The superiority of AMDE-BP-FNN is highlighted by quantitative comparison.
This study established an adaptive memetic differential evolution-back propagation-fuzzy neural network (AMDE-BP-FNN) control method to achieve high-efficiency and precise control of robots with complex dynamic characteristics while reducing control costs. The adaptive differential evolution (ADE) method was applied to search the optimal parameters in the global scope and delimited the pseudo-global search scope. The memetic differential evolution (MDE) method was used to search for optimal parameters in the pseudo-global scope, and the probability factor was set to decide whether to use the back propagation (BP) algorithm for online optimization. Finally, simulations, experiments, and real-world applications were conducted. The results indicated the high efficiency, high precision, and viability of the proposed AMDE-BP-FNN method. |
| ArticleNumber | 118940 |
| Author | Zheng, Kunming Zeng, Shuisheng Zhang, Qiuju Peng, Li |
| Author_xml | – sequence: 1 givenname: Kunming surname: Zheng fullname: Zheng, Kunming email: zhengkunming_111@jiangnan.edu.cn organization: School of Mechanical Engineering, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, China – sequence: 2 givenname: Qiuju surname: Zhang fullname: Zhang, Qiuju email: qjzhang_jnu@yeah.net organization: School of Mechanical Engineering, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, China – sequence: 3 givenname: Li surname: Peng fullname: Peng, Li email: li_peng_jnu@yeah.net organization: School of Internet of Things Engineering, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, China – sequence: 4 givenname: Shuisheng surname: Zeng fullname: Zeng, Shuisheng email: shuisheng_zeng@yeah.net organization: Changzhou Gucoi Intelligent Equipment Technology Research Institute CO., LTD, 213001 Changzhou, China |
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| Keywords | Memetic evolution algorithm Differential evolution algorithm Back propagation algorithm Fuzzy neural network Robot |
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