This paper proposes a new predictive control system using recurrent RBF networks ( RRBFN ) and Fuzzy rules . This system is constructed from two kinds ef prediCtion systems and a 恥 zzy control system , The prediction systems are constructed from a shor レ te皿 prediction system and a long− term predictton system , In the short . term prediction system , a RRBFN is illputted七he current state of a controlled object , and learns to output the next state of it . 工 nthe long − term prediction system , the RRBFN ts inputted the state of a controlled object 】 that was predicted by the short −term prediction system , and predicts the next state of it, The long −term pred正 ction system is repeated this operation n times , and predicts the state of the controlled object on time t十 n . The Fuzzy contro15ystem con 七rols the contrel1ed object based on the prediction results of the long一 七erm prediction system ・We test the proposed method under an outfielder problem in order to investigate its e 缶 ciency . Key Werds: Learning , Neural networkS , 恥 zzy control
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.