2015
DOI: 10.1007/s00500-015-1688-3
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Learning a robot controller using an adaptive hierarchical fuzzy rule-based system

Abstract: The majority of machine learning techniques applied to learning a robot controller generalise over either a uniform or pre-defined representation that is selected by a human designer. The approach taken in this paper is to reduce the reliance on the human designer by adapting the representation to improve the generalisation during the learning process. An extension of a Hierarchical Fuzzy Rule-Based System (HFRBS) is proposed that identifies and refines inaccurate regions of a fuzzy controller, while interacti… Show more

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Cited by 8 publications
(3 citation statements)
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“…2) Advantages of Fuzzy Logic System: Additionally, FLSs have gained acceptance as a methodology for designing robust controllers that can effectively handle uncertainty and imprecision [13], [14]. They have been applied to various problems and have demonstrated their ability to generate more resilient and cost-effective solutions in the face of uncertainties.…”
Section: A Fuzzy Logic Systemmentioning
confidence: 99%
“…2) Advantages of Fuzzy Logic System: Additionally, FLSs have gained acceptance as a methodology for designing robust controllers that can effectively handle uncertainty and imprecision [13], [14]. They have been applied to various problems and have demonstrated their ability to generate more resilient and cost-effective solutions in the face of uncertainties.…”
Section: A Fuzzy Logic Systemmentioning
confidence: 99%
“…Hierarchical fuzzy evaluation methods are widely used in learning robot control, construction goals and strategies, control systems, wind turbines, and power loads. The approach used in the paper was based on extending Hierarchical Fuzzy Rule-Based Systems, which learned a controller with a variable internal representation for both supervised and reinforcement learning problems while interacting with the environment (Waldock & Carse, 2016). Liu et al (2019) built the combination of multiobjective and optimization strategies and improved the comprehensive fuzzy evaluation to realize the ecological operation decision of the reservoir and the control system on the instance of Learning Management System.…”
Section: Introductionmentioning
confidence: 99%
“…Problems like navigation from the narrow passages have effectively solved by Huq et al (2008). Fuzzy logic has been used with the combination with the sensor-based navigation technique (Gasos and Rosetti, 1999;Demirli and Turksen, 2000;Carelli and Freire, 2003;Waldock and Carse, 2015) to improve the incremental learning of the new environment. Recently, fuzzy triangulation method (Yaonan et al, 2011) and reinforced-based navigation (Jaradat et al, 2011) have been developed which helps to minimize the angular uncertainty and radial uncertainty present in the environment.…”
Section: Introductionmentioning
confidence: 99%