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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Veröffentlicht in:Information sciences Jg. 637; S. 118940
Hauptverfasser: Zheng, Kunming, Zhang, Qiuju, Peng, Li, Zeng, Shuisheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.08.2023
Schlagworte:
Back propagation algorithm
Differential evolution algorithm
Fuzzy neural network
Memetic evolution algorithm
Robot
Memetic evolution algorithm
Differential evolution algorithm
Back propagation algorithm
Fuzzy neural network
Robot
ISSN:0020-0255, 1872-6291
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Zusammenfassung:•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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2023.118940