Multigrid-based fuzzy systems for time series prediction: CATS competition

Author

Herrera, L.J. ; Pomares, H. ; Rojas, I. ; Gonzalez, Jose ; Awad, M. ; Herrera, A.

Author_Institution

Dept. of Comput. Archit. & Comput. Technol., Granada Univ., Spain

Volume

2

fYear

2004

fDate

25-29 July 2004

Firstpage

1603

Abstract

In this paper, the multigrid-based fuzzy system (MGFS) approach is applied for the CATS time series prediction benchmark. The MGFS architecture overcomes the problem inherent to all grid-based fuzzy systems when dealing with high dimensional input data, thus keeping low computational cost and high performance. A greedy algorithm for MGFS structure identification allows to perform the input variable selection for the time series prediction problem, while identifying the pseudo-optimal architecture according to the provided dataset.

Keywords

differential equations; function approximation; fuzzy systems; greedy algorithms; identification; time series; competition on artificial time series; greedy algorithm; multigrid based fuzzy system architecture; pseudo optimal architecture; structure identification; time series prediction; Cats; Computational efficiency; Computer architecture; Electronic mail; Function approximation; Fuzzy systems; Input variables; Mean square error methods; Predictive models; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-8359-1

Type

conf

DOI

10.1109/IJCNN.2004.1380197

Filename

1380197