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RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 12, 页码: 5116-5126
作者:  Zhang, Jiazheng;  Jin, Long;  Cheng, Long
收藏  |  浏览/下载：231/0  |  提交时间：2021/03/02
Optimization  Neural networks  Task analysis  Nash equilibrium  Manipulator dynamics  Distributed control  game theory  manipulability optimization  neural network  redundancy resolution  
Complex-Valued Discrete-Time Neural Dynamics for Perturbed Time-Dependent Complex Quadratic Programming With Applications 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 9, 页码: 3555-3569
作者:  Qi, Yimeng;  Jin, Long;  Wang, Yaonan;  Xiao, Lin;  Zhang, Jiliang
收藏  |  浏览/下载：171/0  |  提交时间：2021/01/07
Computational modeling  Convergence  Mathematical model  Recurrent neural networks  Perturbation methods  Robots  Numerical models  Complex domain  discrete-time neural dynamics (DTND)  quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS)  quadratic programming (QP)  
Neural Dynamics for Control of Industrial Agitator Tank With Rapid Convergence and Perturbations Rejection 期刊论文
IEEE ACCESS, 2019, 卷号: 7, 页码: 102941-102950
作者:  Duan, Wenhui;  Xiao, Xiuchun;  Fu, Dongyang;  Yan, Jingwen;  Liu, Mei;  Zhang, Jiliang;  Jin, Long
收藏  |  浏览/下载：206/0  |  提交时间：2019/12/16
Chemical industry  automatic control  control design  neural dynamics method  rapid convergence  perturbations rejection