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

Xiaoxiao Li, Shuai Li Orcid Logo, Zhihao Xu, Xuefeng Zhou

Neural Processing Letters, Volume: 53, Issue: 2, Pages: 1287 - 1304

Swansea University Author: Shuai Li Orcid Logo

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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....

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Published in: Neural Processing Letters
ISSN: 1370-4621 1573-773X
Published: Springer Science and Business Media LLC 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa56362
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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.
Keywords: Zhang neural network; Varying-parameter convergence-accelerated neural network; Noise-resistant; Dynamic matrix pseudoinverse
College: College of Engineering
Issue: 2
Start Page: 1287
End Page: 1304