Open AccessPrediction of multivariable chaotic time series using optimized extreme learning machine
Author(s) -
Guangyong Gao,
Guoping Jiang
Publication year - 2012
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.61.040506
Subject(s) - multivariable calculus , chaotic , extreme learning machine , generalization , computer science , series (stratigraphy) , time series , noise (video) , algorithm , control theory (sociology) , artificial intelligence , artificial neural network , machine learning , mathematics , control engineering , paleontology , control (management) , engineering , mathematical analysis , image (mathematics) , biology
A prediction algorithm of multivariable chaotic time series is proposed based on optimized extreme learning machine (ELM). In this algorithm, a presented composite chaos system and mutative scale chaos method are utilized first to search and optimize the parameters of ELM for improving the generalization performance. Then the optimized ELM is used to predict the multivariable chaotic time series of Rossler coupled system for single step and muti-step, and the scheme is compared with the congeneric method, which shows the validity and stronger ability against noise of the developed algorithm. Finally, the relation between prediction result and number of hidden neurons is discussed.