Open AccessA neuro-fuzzy method for predicting the chaotic time series
Author(s) -
Yuxia Hu,
Jinfeng Gao
Publication year - 2005
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.54.5034
Subject(s) - neuro fuzzy , chaotic , computer science , fuzzy logic , defuzzification , adaptive neuro fuzzy inference system , series (stratigraphy) , adaptability , artificial neural network , time series , fuzzy set operations , fuzzy rule , fuzzy number , lorenz system , artificial intelligence , fuzzy classification , fuzzy set , fuzzy control system , machine learning , paleontology , ecology , biology
A neuro-fuzzy approach based on a novel hybrid learning method is presented, which can generate the best fuzzy rule set automatically from the desired input-output data pairs only and can give the initial neuro-fuzzy system and the initial parameters of fuzzy membership functions. Then the parameters of fuzzy membership functions and the weights can be easily tuned by employing neural network's self-learning techniques. This approach reduces the rule matching time and accelerates the speed of the fuzzy logic referencing and improves the adaptability of the neuro-fuzzy system. Using the proposed neuro-fuzzy system and the learning algorithms we simulated the prediction of the Lorenz chaotic time series, the results demonstrate the effectiveness of the chaotic time series prediction approach.