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

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 per...

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Bibliographic Details
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
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Summary: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.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2899428