Fuzzy Markov Predictor with First and Second-Order Dependences

S
SBRN
2002
VII Brazilian Symposium on Neural Networks (SBRN'02)
Abstract - Fuzzy Markov Predictor with First and Second-Order Dependences

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	Similar Articles Articles by Marcelo Andrade Teixeira Articles by Gerson Zaverucha

VII Brazilian Symposium on Neural Networks (SBRN'02)

Pernambuco, Brazil

November 11-November 14

ISBN: 0-7695-1709-9

	ASCII Text	x
	Marcelo Andrade Teixeira, Gerson Zaverucha, "Fuzzy Markov Predictor with First and Second-Order Dependences," Neural Networks, Brazilian Symposium on, pp. 80, VII Brazilian Symposium on Neural Networks (SBRN'02), 2002.

	BibTex	x
	@article{ 10.1109/SBRN.2002.1181439, author = {Marcelo Andrade Teixeira and Gerson Zaverucha}, title = {Fuzzy Markov Predictor with First and Second-Order Dependences}, journal ={Neural Networks, Brazilian Symposium on}, volume = {0}, year = {2002}, isbn = {0-7695-1709-9}, pages = {80}, doi = {http://doi.ieeecomputersociety.org/10.1109/SBRN.2002.1181439}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Neural Networks, Brazilian Symposium on TI - Fuzzy Markov Predictor with First and Second-Order Dependences SN - 0-7695-1709-9 SP EP A1 - Marcelo Andrade Teixeira, A1 - Gerson Zaverucha, PY - 2002 KW - null VL - 0 JA - Neural Networks, Brazilian Symposium on ER -

Marcelo Andrade Teixeira, Federal University of Rio de Janeiro (UFRJ); Electric Power Research Center (CEPEL)

Gerson Zaverucha, Federal University of Rio de Janeiro (UFRJ)

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SBRN.2002.1181439

We present two new versions of the Fuzzy Markov Predictor (FMP) with different dependences among the inputs: first-order and second-order dependences. The FMP is a modification of the Hidden Markov Model in order to enable it to predict numerical values. The FMP can be seen as an extension of the Fuzzy Bayes Predictor (FBP). These hybrid systems are applied to the task of monthly electric load forecasting and successfully compared with one fuzzy system, and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.

Citation:

Marcelo Andrade Teixeira, Gerson Zaverucha, "Fuzzy Markov Predictor with First and Second-Order Dependences," sbrn, pp.80, VII Brazilian Symposium on Neural Networks (SBRN'02), 2002

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