Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74240
Title: Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms
Authors: Jin, L 
Li, S 
Luo, X
Shang, MS
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: Proceedings of the International Joint Conference on Neural Networks, 2017, 7966382, p. 4165-4170 How to cite?
Abstract: This paper investigates the distributed motion planning of multiple robot arms with limited communications in the presence of noises. To do this, a nonlinearly-activated noise-tolerant zeroing neural network (NANTZNN) is designed and presented for the first time for solving the presented distributed scheme online. Theoretical analyses and simulation results show the effectiveness and accuracy of the presented distributed scheme with the aid of NANTZNN model.
URI: http://hdl.handle.net/10397/74240
ISBN: 9781509061815
DOI: 10.1109/IJCNN.2017.7966382
Appears in Collections:Conference Paper

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