In this paper, we present a study on the use of fuzzy neural networks and their application to the prediction of times series generated by complex processes of the real-world. The new learning strategy is suited to any fuzzy inference model, especially in the case of higher-order Sugeno-type fuzzy rules. The data considered herein are real-world cases concerning chaotic benchmarks as well as environmental time series. The comparison with respect to well-known neural and fuzzy neural models will prove that our approach is able to follow the behavior of the underlying, unknown process with a good prediction of the observed time series.

A new learning approach for Takagi-Sugeno fuzzy systems applied to time series prediction / Altilio, Rosa; Rosato, Antonello; Panella, Massimo. - STAMPA. - (2017), pp. 1-6. (Intervento presentato al convegno IEEE International Conference on Fuzzy Systems tenutosi a Napoli, Italia nel 9-12 luglio 2017) [10.1109/FUZZ-IEEE.2017.8015723].

A new learning approach for Takagi-Sugeno fuzzy systems applied to time series prediction

ALTILIO, ROSA;ROSATO, ANTONELLO;PANELLA, Massimo
2017

Abstract

In this paper, we present a study on the use of fuzzy neural networks and their application to the prediction of times series generated by complex processes of the real-world. The new learning strategy is suited to any fuzzy inference model, especially in the case of higher-order Sugeno-type fuzzy rules. The data considered herein are real-world cases concerning chaotic benchmarks as well as environmental time series. The comparison with respect to well-known neural and fuzzy neural models will prove that our approach is able to follow the behavior of the underlying, unknown process with a good prediction of the observed time series.
2017
IEEE International Conference on Fuzzy Systems
Feedback control; forecasting; fuzzy logic
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A new learning approach for Takagi-Sugeno fuzzy systems applied to time series prediction / Altilio, Rosa; Rosato, Antonello; Panella, Massimo. - STAMPA. - (2017), pp. 1-6. (Intervento presentato al convegno IEEE International Conference on Fuzzy Systems tenutosi a Napoli, Italia nel 9-12 luglio 2017) [10.1109/FUZZ-IEEE.2017.8015723].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/987109
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