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
Hlavní autoři:	Juang, Chia-Feng, Bui, Trong Bac
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
ISSN:	1063-6706, 1941-0034
On-line přístup:	Získat plný text
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