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An efficient hybridization of Genetic Algorithms and Particle Swarm Optimization for inverse kinematics[C]//Proceedings of the 2016 IEEE International Conference on Robotics and Bio￾mimetics. Qingdao, China: IEEE, 2017: 1782–1789. [8] HALALI M A, AZARI V, ARABLOO M, et al. Applica￾tion of a radial basis function neural network to estimate pressure gradient in water-oil pipelines[J]. Journal of the Taiwan institute of chemical engineers, 2016, 58: 189–202. [9] CHIDDARWAR S S, BABU N R. Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach[J]. En￾gineering applications of artificial intelligence, 2010, 23(7): 1083–1092. [10] ZUBIZARRETA A, LARREA M, IRIGOYEN E, et al. Real time direct kinematic problem computation of the 3PRS robot using neural networks[J]. Neurocomputing, 2017, 271: 104–114. [11] TOSHANI H, FARROKHI M. Real-time inverse kin￾ematics of redundant manipulators using neural networks and quadratic programming: a Lyapunov-based approach[J]. Robotics and autonomous systems, 2014, 62(6): 766–781. [12] KÖKER R. A genetic algorithm approach to a neural-net￾work-based inverse kinematics solution of robotic manip- [13] ulators based on error minimization[J]. Information sci￾ences, 2013, 222: 528–543. JIA Weikuan, ZHAO Dean, DING Ling. An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample[J]. Applied soft computing, 2016, 48: 373–384. [14] ZAPLANA I, BASANEZ L. A novel closed-form solu￾tion for the inverse kinematics of redundant manipulators through workspace analysis[J]. Mechanism and machine theory, 2018, 121: 829–843. [15] 赵建强, 刘满禄, 王姮. 基于 PSO 优化 BP 神经网络的 逆运动学求解研究[J]. 自动化与仪表, 2016, 31(11): 1–6. ZHAO Jianqiang, LIU Manlu, WANG Heng. Research on inverse kinematics solution of BP neural network based on PSO optimization[J]. Automation and instrumentation, 2016, 31(11): 1–6. [16] 乔俊飞, 安茹, 韩红桂. 基于相对贡献指标的自组织 RBF 神经网络的设计[J]. 智能系统学报, 2018, 13(2): 159–167. QIAO Junfei, AN Ru, HAN Honggui. Design of self-or￾ganizing RBF neural network based on relative contribu￾tion index[J]. CAAI transactions on intelligent systems, 2018, 13(2): 159–167. [17] 作者简介： 张毅，男，1966 年生，教授，主要 研究方向为智能机器人、数据融合、信 息无障碍技术。主持国家、省部级以 及产学研等各种项目 10 余项。发表 学术论文 100 余篇，被 SCI、EI 和 ISTP 收录 30 余篇。出版著作 5 部。 刘芳君，女，1990 年生，硕士研究 生，主要研究方向为工业机器人轨迹 规划。 胡磊，男，1994 年生，硕士研究 生，主要研究方向为工业机器人运动 学控制。 ·170· 智 能 系 统 学 报 第 14 卷