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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2021

19.02.2021

A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application

verfasst von: Xiaoxiao Li, Shuai Li, Zhihao Xu, Xuefeng Zhou

Erschienen in: Neural Processing Letters | Ausgabe 2/2021

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Abstract

Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse.
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Metadaten
Titel
A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application
verfasst von
Xiaoxiao Li
Shuai Li
Zhihao Xu
Xuefeng Zhou
Publikationsdatum
19.02.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10440-x

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