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Neural Computing and Applications

A novel varying-parameter periodic rhythm neural network for solving time-varying matrix equation in finite energy noise environment and its application to robot arm

  • 14-08-2023
  • Original Article
Published in:
Authors
Chunquan Li
Boyu Zheng
Qingling Ou
Qianqian Wang
Chong Yue
Limin Chen
Zhijun Zhang
Junzhi Yu
Peter X. Liu
Published in
Neural Computing and Applications | Issue 30/2023

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Abstract

The article introduces a novel varying-parameter periodic rhythm neural network (VP-PRNN) designed to solve time-varying matrix equations (TVME) in noisy environments. Unlike existing methods that primarily address periodic noise, the VP-PRNN effectively handles both periodic and aperiodic finite energy noise. The VP-PRNN employs time-varying parameters to enhance convergence speed and demonstrates superior denoising ability and convergence rates compared to traditional ZNN and CRLN models. Theoretical analysis and experimental results validate the VP-PRNN's global convergence and robustness in various noise conditions. Additionally, the VP-PRNN is applied to the inverse kinematic planning of a robotic arm, showcasing its practical applicability and superior performance in real-world scenarios.
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previous article ACRE: Actor-Critic with Reward-Preserving Exploration
next article Photoelectric factor prediction using automated learning and uncertainty quantification
Title
A novel varying-parameter periodic rhythm neural network for solving time-varying matrix equation in finite energy noise environment and its application to robot arm
Authors
Chunquan Li
Boyu Zheng
Qingling Ou
Qianqian Wang
Chong Yue
Limin Chen
Zhijun Zhang
Junzhi Yu
Peter X. Liu
Publication date
14-08-2023
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 30/2023
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-023-08895-1
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