Reinforcement Neural Fuzzy Surrogate-Assisted Multiobjective Evolutionary Fuzzy Systems With Robot Learning Control Application
This paper proposes a new reinforcement neural fuzzy surrogate (RNFS)-assisted multiobjective evolutionary optimization (RNFS-MEO) algorithm to boost the learning efficiency of data-driven fuzzy controllers (FCs). The RNFS-MEO is applied to evolve a population of FCs in a multiobjective robot wall-f...
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| Vydáno v: | IEEE transactions on fuzzy systems Ročník 28; číslo 3; s. 434 - 446 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1063-6706, 1941-0034 |
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| Abstract | This paper proposes a new reinforcement neural fuzzy surrogate (RNFS)-assisted multiobjective evolutionary optimization (RNFS-MEO) algorithm to boost the learning efficiency of data-driven fuzzy controllers (FCs). The RNFS-MEO is applied to evolve a population of FCs in a multiobjective robot wall-following control problem in order to reduce the number of time-consuming control trials and the implementation time of learning. In the RNFS-MEO, the RNFS is incorporated into a typical multiobjective continuous ant colony optimization algorithm to improve its learning efficiency. The RNFS estimates the accumulated multiobjective function values of the FCs in a colony without applying them to control a process, which helps reduce the number of control trials. The RNFS is trained online through structure and parameter learning based on the reinforcement signals from controlling a process. Considering the influence of the current control signals on the future states of a controlled process, the temporal difference technique is used in the RNFS training so that it estimates not only the current but also the future objective function values. The colony of FCs in the RNFS-MEO is repeatedly evolved based on the RNFS estimated values or the objective function values from real evaluations until a colony of successful FCs is found. The RNFS-MEO-based FC learning approach is applied to a robot wall-following control problem. Simulations and experiments on the robot control application are performed to verify the effectiveness and efficiency of the RNFS-MEO. |
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| AbstractList | This paper proposes a new reinforcement neural fuzzy surrogate (RNFS)-assisted multiobjective evolutionary optimization (RNFS-MEO) algorithm to boost the learning efficiency of data-driven fuzzy controllers (FCs). The RNFS-MEO is applied to evolve a population of FCs in a multiobjective robot wall-following control problem in order to reduce the number of time-consuming control trials and the implementation time of learning. In the RNFS-MEO, the RNFS is incorporated into a typical multiobjective continuous ant colony optimization algorithm to improve its learning efficiency. The RNFS estimates the accumulated multiobjective function values of the FCs in a colony without applying them to control a process, which helps reduce the number of control trials. The RNFS is trained online through structure and parameter learning based on the reinforcement signals from controlling a process. Considering the influence of the current control signals on the future states of a controlled process, the temporal difference technique is used in the RNFS training so that it estimates not only the current but also the future objective function values. The colony of FCs in the RNFS-MEO is repeatedly evolved based on the RNFS estimated values or the objective function values from real evaluations until a colony of successful FCs is found. The RNFS-MEO-based FC learning approach is applied to a robot wall-following control problem. Simulations and experiments on the robot control application are performed to verify the effectiveness and efficiency of the RNFS-MEO. |
| Author | Juang, Chia-Feng Bui, Trong Bac |
| Author_xml | – sequence: 1 givenname: Chia-Feng orcidid: 0000-0002-3713-4315 surname: Juang fullname: Juang, Chia-Feng email: cfjuang@dragon.nchu.edu.tw organization: Department of Electrical Engineering, National Chung-Hsing University, Taichung, Taiwan, R.O.C – sequence: 2 givenname: Trong Bac surname: Bui fullname: Bui, Trong Bac email: buitrongbac@gmail.com organization: Department of Electrical Engineering, National Chung-Hsing University, Taichung, Taiwan, R.O.C |
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| SubjectTerms | Ant colony optimization Artificial neural networks Computer simulation Efficiency Evolutionary algorithms Evolutionary fuzzy systems (EFS) Fuzzy control Fuzzy logic Fuzzy systems Machine learning Mathematical model multiobjective optimization algorithms Multiple objective analysis neural fuzzy systems Optimization Process control Robot control Robot learning Robots Signal processing surrogate-assisted learning temporal difference (TD) learning Training |
| Title | Reinforcement Neural Fuzzy Surrogate-Assisted Multiobjective Evolutionary Fuzzy Systems With Robot Learning Control Application |
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