Title :
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
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;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1380197
