Open AccessPrediction of chaotic time series based on hierarchical fuzzy-clustering
Author(s) -
Fucai Liu,
Liping Sun,
Liang Xiao-Ming
Publication year - 2006
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.55.3302
Subject(s) - chaotic , computer science , fuzzy logic , fuzzy clustering , series (stratigraphy) , cluster analysis , partition (number theory) , algorithm , time series , sequence (biology) , data mining , mathematics , artificial intelligence , machine learning , paleontology , genetics , combinatorics , biology
The paper introduces a new method for fuzzy modeling based on a hierarchical fuzzy-clustering scheme. The method consists of a sequence of steps aiming at developing a Takagi-Sugeno (TS) fuzzy model of optimal structure. The premise parameters' identification consists of two steps: Start from an initial fuzzy partition of input space by a nearest-neighbor clustering method to get the number of rules and the initial clustering center; then premise parameters are further processed using a fuzzy C-means algorithm (FCM). The conclusion parameters are identified by the weighted least square method and further optimized by selective recursive least square method. To illustrate the performance of the proposed method, simulations on chaotic Mackey-Glass time series prediction are performed. The results show that the chaotic Mackey-Glass time series are accurately predicted, which demonstrates the effectiveness of this method.