Open AccessChaotic time series prediction based on wavelet echo state network
Author(s) -
Tong Su,
Li Han
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.61.080506
Subject(s) - echo state network , series (stratigraphy) , chaotic , generalization , computer science , lorenz system , echo (communications protocol) , wavelet , time series , state (computer science) , reservoir computing , algorithm , noise (video) , artificial intelligence , artificial neural network , mathematics , machine learning , recurrent neural network , paleontology , mathematical analysis , computer network , image (mathematics) , biology
Chaos is widespread in nature and human society, so the prediction of chaotic time series is very important. In this paper, we propose a new chaotic time series prediction model echo state network based on wavelet, which can effectively overcome the ill-posed problem that exists in traditional echo state networks. And it also has a good generalization ability. Three time series are used to test the new model, i.e., Lorenz time series, Lorenz time series with added noise and batch reactor vessel temperature time series. Results suggest that the new proposed method can achieve a higher predictable accuracy, better generalization and more stable prediction results than traditional echo state networks.