Abstract
This paper presents an evolutionary approach to learning a fuzzy logic controller(FLC) employed for reactive behaviour control of Sony legged robots. The learning scheme is divided into two stages. The first stage is a structure learning in which the rule base of FLC is generated by a backup updating learning. The second stage is a parameter learning in which the parameters of membership functions of fuzzy sets are learned by a genetic algorithm (GA). Simulation results are provided to show the effectiveness of the proposed learning scheme.
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© 2002 Springer-Verlag Berlin Heidelberg
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Gu, D., Hu, H. (2002). Evolving Fuzzy Logic Controllers for Sony Legged Robots. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds) RoboCup 2001: Robot Soccer World Cup V. RoboCup 2001. Lecture Notes in Computer Science(), vol 2377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45603-1_43
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DOI: https://doi.org/10.1007/3-540-45603-1_43
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