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An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots
The brain emotional learning (BEL) system was inspired by the biological amygdala-orbitofrontal model to mimic the high speed of the emotional learning mechanism in the mammalian brain, which has been successfully applied in many real-world applications. Despite of its success, such system often suf...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369368/ https://www.ncbi.nlm.nih.gov/pubmed/30778294 http://dx.doi.org/10.3389/fnbot.2019.00002 |
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author | Fang, Wubing Chao, Fei Lin, Chih-Min Yang, Longzhi Shang, Changjing Zhou, Changle |
author_facet | Fang, Wubing Chao, Fei Lin, Chih-Min Yang, Longzhi Shang, Changjing Zhou, Changle |
author_sort | Fang, Wubing |
collection | PubMed |
description | The brain emotional learning (BEL) system was inspired by the biological amygdala-orbitofrontal model to mimic the high speed of the emotional learning mechanism in the mammalian brain, which has been successfully applied in many real-world applications. Despite of its success, such system often suffers from slow convergence for online humanoid robotic control. This paper presents an improved fuzzy BEL model (iFBEL) neural network by integrating a fuzzy neural network (FNN) to a conventional BEL, in an effort to better support humanoid robots. In particular, the system inputs are passed into a sensory and emotional channels that jointly produce the final outputs of the network. The non-linear approximation ability of the iFBEL is achieved by taking the BEL network as the emotional channel. The proposed iFBEL works with a robust controller in generating the hand and gait motion of a humanoid robot. The updating rules of the iFBEL-based controller are composed of two parts, including a sensory channel followed by the updating rules of the conventional BEL model, and the updating rules of the FNN and the robust controller which are derived from the “Lyapunov” function. The experiments on a three-joint robot manipulator and a six-joint biped robot demonstrated the superiority of the proposed system in reference to a conventional proportional-integral-derivative controller and a fuzzy cerebellar model articulation controller, based on the more accurate and faster control performance of the proposed iFBEL. |
format | Online Article Text |
id | pubmed-6369368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63693682019-02-18 An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots Fang, Wubing Chao, Fei Lin, Chih-Min Yang, Longzhi Shang, Changjing Zhou, Changle Front Neurorobot Neuroscience The brain emotional learning (BEL) system was inspired by the biological amygdala-orbitofrontal model to mimic the high speed of the emotional learning mechanism in the mammalian brain, which has been successfully applied in many real-world applications. Despite of its success, such system often suffers from slow convergence for online humanoid robotic control. This paper presents an improved fuzzy BEL model (iFBEL) neural network by integrating a fuzzy neural network (FNN) to a conventional BEL, in an effort to better support humanoid robots. In particular, the system inputs are passed into a sensory and emotional channels that jointly produce the final outputs of the network. The non-linear approximation ability of the iFBEL is achieved by taking the BEL network as the emotional channel. The proposed iFBEL works with a robust controller in generating the hand and gait motion of a humanoid robot. The updating rules of the iFBEL-based controller are composed of two parts, including a sensory channel followed by the updating rules of the conventional BEL model, and the updating rules of the FNN and the robust controller which are derived from the “Lyapunov” function. The experiments on a three-joint robot manipulator and a six-joint biped robot demonstrated the superiority of the proposed system in reference to a conventional proportional-integral-derivative controller and a fuzzy cerebellar model articulation controller, based on the more accurate and faster control performance of the proposed iFBEL. Frontiers Media S.A. 2019-02-04 /pmc/articles/PMC6369368/ /pubmed/30778294 http://dx.doi.org/10.3389/fnbot.2019.00002 Text en Copyright © 2019 Fang, Chao, Lin, Yang, Shang and Zhou. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Fang, Wubing Chao, Fei Lin, Chih-Min Yang, Longzhi Shang, Changjing Zhou, Changle An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots |
title | An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots |
title_full | An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots |
title_fullStr | An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots |
title_full_unstemmed | An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots |
title_short | An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots |
title_sort | improved fuzzy brain emotional learning model network controller for humanoid robots |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369368/ https://www.ncbi.nlm.nih.gov/pubmed/30778294 http://dx.doi.org/10.3389/fnbot.2019.00002 |
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