New Zeroing Neural Network Models for Solving Nonstationary Sylvester Equation With Verifications on Mobile Manipulators

• Published in: IEEE transactions on industrial informatics 2019-09, Vol.15 (9), p.5011-5022
• Main Authors: Yan, Xiaogang, Liu, Mei, Jin, Long, Li, Shuai, Hu, Bin, Zhang, Xin, Huang, Zhiguan
• Format: Article
• Language: English
• Subjects: Analytical models; Computational modeling; Computer simulation; Convergence; Economic models; Finite-time convergence; Informatics; Mathematical model; Neural networks; nonstationary Sylvester equation; Performance enhancement; recurrent neural network (RNN); Recurrent neural networks; saturation activation functionzeroing neural network (ZNN); Upper bounds; Wireless networks
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2899428

Recurrent neural networks (RNNs) have found a great variety of application areas. As a special type of RNNs, zeroing neural network (ZNN), or termed Zhang neural network, has been reported to have powerful abilities to address various nonstationary problems. To overcome drawbacks and improve the performance of existing ZNN models, several modified ZNN models are proposed in this paper, which allow nonconvex activation functions and possess accelerated finite-time convergence property. Theoretical analyses suggest that the developed ZNN models are equipped with the global convergence property and the convergence-accelerated models are verified by the estimated upper bounds of convergence time. Finally, comparative and illustrative simulation results, including a verification on a mobile manipulator, are presented to illustrate the effectiveness and superiority of proposed ZNN models to existing models for solving nonstationary Sylvester equations.