Backpropagation based training algorithm for Takagi-Sugeno type MIMO neuro-fuzzy network to forecast electrical load time series

Author

Palit, Ajoy Kumar ; Doeding, Gerhard ; Anheier, Walter ; Popovic, Dobrivoje

Author_Institution

deneg GmbH, Bremen, Germany

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

86

Lastpage

91

Abstract

Describes a backpropagation based algorithm that can be used to train the Takagi-Sugeno (TS) type multi-input multi-output (MIMO) neuro-fuzzy network efficiently. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than that the simple backpropagation algorithm (BPA). Finally, the above training algorithm is tested on neuro-fuzzy modeling and forecasting application of electrical load time series

Keywords

MIMO systems; backpropagation; fuzzy logic; fuzzy neural nets; identification; load forecasting; time series; Takagi-Sugeno type MIMO neuro-fuzzy network; backpropagation based training algorithm; electrical load time series; performance index; sum squared error; Backpropagation algorithms; Electronic mail; Fuzzy logic; Fuzzy neural networks; Load forecasting; MIMO; Neural networks; Noise measurement; Predictive models; Takagi-Sugeno model;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7280-8

Type

conf

DOI

10.1109/FUZZ.2002.1004965

Filename

1004965