Noise-tolerant neural algorithm for online solving time-varying full-rank matrix Moore–Penrose inverse problems: A control-theoretic approach

In this paper, zeroing neural network models are redesigned and analyzed from a control-theoretical framework for online solving time-varying full-rank Moore–Penrose inversions. To solve time-varying full-rank Moore–Penrose inverse problems with different noises in real time, some modified zeroing n...

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Vydáno v:Neurocomputing (Amsterdam) Ročník 413; s. 158 - 172
Hlavní autoři: Sun, Zhongbo, Li, Feng, Jin, Long, Shi, Tian, Liu, Keping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 06.11.2020
Témata:
Exponential convergence
Moore–Penrose inverse problems
Recurrent neural network model
Redundant robot manipulators
Time-varying full-rank matrix
Moore–Penrose inverse problems
Exponential convergence
Recurrent neural network model
Redundant robot manipulators
Time-varying full-rank matrix
ISSN:0925-2312, 1872-8286
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Shrnutí:In this paper, zeroing neural network models are redesigned and analyzed from a control-theoretical framework for online solving time-varying full-rank Moore–Penrose inversions. To solve time-varying full-rank Moore–Penrose inverse problems with different noises in real time, some modified zeroing neural network models are developed, analyzed and investigated from the perspective of control. Furthermore, the proposed zeroing neural network models globally converge to the theoretical solution of the full-rank Moore–Penrose inverse problem without noises, and exponentially converge to the exact solution in the presence of noises, which are demonstrated theoretically. Moreover, in comparison with existing models, numerical simulations are provided to substantiate the feasibility and superiority of the proposed modified neural network for online solving time-varying full-rank Moore–Penrose problems with inherent tolerance to noises. In addition, the numerical results infer that different activation functions can be applied to accelerate the convergence speed of the zeroing neural network model. Finally, the proposed zeroing neural network models are applied to the motion generation of redundant robot manipulators, which illustrates its high efficiency and robustness.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.06.050