Chaotic Time Series Prediction using Extended Fuzzy Entropy Clustering

확장된 퍼지엔트로피 클러스터링을 이용한 카오스 시계열 데이터 예측

In this paper, we propose new algorithms for the partition of input space and the generation of fuzzy control rules. The one consists of Shannon and extended fuzzy entropy function, the other consists of adaptive fuzzy neural system with back propagation teaming rule. The focus of this scheme is to realize the optimal fuzzy rule base with the minimal number of the parameters of the rules, reducing the complexity of the system. The proposed algorithm is tested with the time series prediction problem using Mackey-Glass chaotic time series.