The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)
Online ISSN : 2424-3124
2014
Session ID : 1A1-X06
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1A1-X06 A Long Term Prediction System using Recurrent RBF Networks(Evolution and Learning for Robotics)
Takuma GOTOKazuaki YAMADAAkihiro MATSUMOTO
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CONFERENCE PROCEEDINGS FREE ACCESS

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Abstract
This paper proposes a new predictive control system using recurrent RBF networks (RRBFN) and Fuzzy rules. This system is constructed from a prediction system and a Fuzzy control system. The prediction system predicts the state of the controlled object on time t+n. The Fuzzy control system controls the controlled object based on the prediction results of the long-term prediction system. We test the proposed method under an outfielder problem in order to investigate its efficiency.
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© 2014 The Japan Society of Mechanical Engineers
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