Adaptive memetic differential evolution-back propagation-fuzzy neural network algorithm for robot control

被引：18

作者：

Zheng, Kunming ^{[1
,2
]}

Zhang, Qiuju ^{[1
,2
]}

Peng, Li ^{[3
]}

Zeng, Shuisheng ^{[4
]}

机构：

[1] Jiangnan Univ, Sch Mech Engn, 1800 Lihu Ave, Wuxi 214122, Peoples R China

[2] Jiangsu Key Lab Adv Food Mfg Equipment & Technol, 1800 Lihu Ave, Wuxi 214122, Peoples R China

[3] Jiangnan Univ, Sch Internet Things Engn, 1800 Lihu Ave, Wuxi 214122, Peoples R China

[4] Changzhou Gucoi Intelligent Equipment Technol Res, Changzhou 213001, Peoples R China

来源：

INFORMATION SCIENCES | 2023年 / 637卷

基金：

美国国家科学基金会;

关键词：

Robot; Memetic evolution algorithm; Differential evolution algorithm; Back propagation algorithm; Fuzzy neural network; LOCAL SEARCH; IDENTIFICATION; OPTIMIZATION; SYSTEMS; DESIGN;

D O I：

10.1016/j.ins.2023.118940

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：19

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