Neuro-Fuzzy Approaches for Forecasting Electrical Load Using Additional Moving Average Window Data Filter on Takagi-Sugeno Type MISO Networks
Felix Pasila*, Ajoy K. Palit**, and Georg Thiele***
*Electrical Engineering Department, Petra Christian University, Siwalankerto 121-131, Surabaya, Indonesia
**Department of Electrical Engineering, ITEM, University of Bremen, D-28359 Bremen, Germany
***Department of Electrical Engineering, IAT, University of Bremen, D-28359 Bremen, Germany
-  J. S. R. Jang, “ANFIS: Adaptive network Based Fuzzy Inference System,” IEEE T. SMC., 23(3): pp. 665-685, 1993.
-  A. K. Palit and R. Babuška, “Efficient training algorithm for Takagi-Sugeno type Neuro-Fuzzy network,” Proc. of FUZZ-IEEE, Vol.3, pp. 1538-1543, 2001.
-  A. K. Palit, G. Doeding, W. Anheier, and D. Popovic, “Backpropagation based training algorithm for Takagi-Sugeno type MIMO neuro-fuzzy network to forecast electrical load time series,” Proc. of FUZZ-IEEE, Honolulu, Hawai, USA. Vol.1, pp. 86-91, 2002.
-  A. K. Palit and D. Popovic, “Computational Intelligence in Time Series Forecasting, Theory and Engineering Applications,” Springer-Verlag, London, pp. 230-246, 2005.
-  F. Pasila, “Forecasting of Electrical Load using Takagi-Sugeno type MIMO Neuro-Fuzzy network,” Master Thesis, University of Bremen, Germany, 2006.
-  M. Setnes, R. Babuška, and U. Kaymark, “Similarity measures in Fuzzy rule base simplification,” IEEE T. System, Man and Cybernetics, Vol.28, pp. 771-775, 1998.
-  D. Xiaosong, D. Popovic, and G. Schulz-Ekloff, “Oscillation resisting in the learning of Backpropagation neural networks,” Proc. of 3rd IFAC/IFIP, Belgium, 1995.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.