Developing a Local Least-Squares Support Vector Machines-Based Neuro-Fuzzy Model for Nonlinear and Chaotic Time Series Prediction

2013 ◽  
Vol 24 (2) ◽  
pp. 207-218 ◽  
Author(s):  
A. Miranian ◽  
M. Abdollahzade
2014 ◽  
Vol 1061-1062 ◽  
pp. 935-938
Author(s):  
Xin You Wang ◽  
Guo Fei Gao ◽  
Zhan Qu ◽  
Hai Feng Pu

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.


2013 ◽  
Vol 62 (12) ◽  
pp. 120511
Author(s):  
Zhao Yong-Ping ◽  
Zhang Li-Yan ◽  
Li De-Cai ◽  
Wang Li-Feng ◽  
Jiang Hong-Zhang

2001 ◽  
Vol 12 (4) ◽  
pp. 809-821 ◽  
Author(s):  
T. Van Gestel ◽  
J.A.K. Suykens ◽  
D.-E. Baestaens ◽  
A. Lambrechts ◽  
G. Lanckriet ◽  
...  

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