1A1-X06 A Long Term Prediction System using Recurrent RBF Networks(Evolution and Learning for Robotics)
Takuma Goto, Kazuaki YAMADA, Akihiro MATSUMOTO · The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2014
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.