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Neural Computing and Applications

Erschienen in:

10.01.2019 | Original Article

Discrete-time neural network with two classes of bias noises for solving time-variant matrix inversion and application to robot tracking

verfasst von: Peng Miao, Deyu Wu, Yanjun Shen, Zhiqiang Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 9/2019

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Abstract

It is well known that noise is inevitable in real world, especially in the case of solving time-variant matrix inversion. Therefore, it is more necessary to study the algorithm with bias noises to solve time-variant matrix inversion. This paper investigates discrete-time neural network with two classes of bias noises for solving time-variant matrix inversion, and its application to robot tracking based on the property of second-order differential equation. Firstly, the model is presented and some indispensable propaedeutics are given. Then, continuous-time and discrete-time neural network with two classes of bias noises is designed, respectively. Their convergence and finite-time stability are also theoretically analyzed. Finally, the proposed models are applied to a five-link robot tracking. Numerical simulations demonstrate the superiority and effectiveness of our method.

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Titel: Discrete-time neural network with two classes of bias noises for solving time-variant matrix inversion and application to robot tracking
verfasst von: Peng Miao
Deyu Wu
Yanjun Shen
Zhiqiang Zhang
Publikationsdatum: 10.01.2019
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 9/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-03986-w

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