中文版 | English
Title

A Supervised-Reinforced Successive Training Framework for a Fuzzy Inference System and Its Application in Robotic Odor Source Searching

Author
Corresponding AuthorFu, Chenglong
Publication Years
2022-05-31
DOI
Source Title
ISSN
1662-5218
Volume16
Abstract

Fuzzy inference systems have been widely applied in robotic control. Previous studies proposed various methods to tune the fuzzy rules and the parameters of the membership functions (MFs). Training the systems with only supervised learning requires a large amount of input-output data, and the performance of the trained system is confined by that of the target system. Training the systems with only reinforcement learning (RL) does not require prior knowledge but is time-consuming, and the initialization of the system remains a problem. In this paper, a supervised-reinforced successive training framework is proposed for a multi-continuous-output fuzzy inference system (MCOFIS). The parameters of the fuzzy inference system are first tuned by a limited number of input-output data from an existing controller with supervised training and then are utilized to initialize the system in the reinforcement training stage. The proposed framework is applied in a robotic odor source searching task and the evaluation results demonstrate that the performance of the fuzzy inference system trained by the successive framework is superior to the systems trained by only supervised learning or RL. The system trained by the proposed framework can achieve around a 10% higher success rate compared to the systems trained by only supervised learning or RL.

Keywords
URL[Source Record]
Indexed By
SCI ; EI
Language
English
SUSTech Authorship
Corresponding
Funding Project
National Key R&D Program of China[2018YFC2001601] ; National Natural Science Foundation of China[
WOS Research Area
Computer Science ; Robotics ; Neurosciences & Neurology
WOS Subject
Computer Science, Artificial Intelligence ; Robotics ; Neurosciences
WOS Accession No
WOS:000810949500001
Publisher
EI Accession Number
20222612262742
EI Keywords
Electronic Nose ; Fuzzy Inference ; Fuzzy Neural Networks ; Fuzzy Systems ; Membership Functions ; Robotics ; Supervised Learning
ESI Classification Code
Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Artificial Intelligence:723.4 ; Expert Systems:723.4.1 ; Robotics:731.5 ; Chemistry:801 ; Mathematics:921 ; Electric And Electronic Instruments:942.1 ; Systems Science:961
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/343058
DepartmentSouthern University of Science and Technology
Affiliation
1.Shenzhen Key Lab Biomimet Robot & Intelligent Syst, Shenzhen, Peoples R China
2.Southern Univ Sci & Technol, Guangdong Prov Key Lab Human Augmentat & Rehabil, Shenzhen, Peoples R China
First Author AffilicationSouthern University of Science and Technology
Corresponding Author AffilicationSouthern University of Science and Technology
Recommended Citation
GB/T 7714
Chen, Xinxing,Leng, Yuquan,Fu, Chenglong. A Supervised-Reinforced Successive Training Framework for a Fuzzy Inference System and Its Application in Robotic Odor Source Searching[J]. Frontiers in Neurorobotics,2022,16.
APA
Chen, Xinxing,Leng, Yuquan,&Fu, Chenglong.(2022).A Supervised-Reinforced Successive Training Framework for a Fuzzy Inference System and Its Application in Robotic Odor Source Searching.Frontiers in Neurorobotics,16.
MLA
Chen, Xinxing,et al."A Supervised-Reinforced Successive Training Framework for a Fuzzy Inference System and Its Application in Robotic Odor Source Searching".Frontiers in Neurorobotics 16(2022).
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