Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Formal Verification of a Fuzzy Rule-Based Classifier Using the Prototype Verification System Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Regularized Fuzzy Neural Network Based on Or Neuron for Time Series Forecasting

verfasst von : Paulo Vitor de Campos Souza, Luiz Carlos Bambirra Torres

Erschienen in: Fuzzy Information Processing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper presents a training algorithm for regularized fuzzy neural networks which is able to generate consistent and accurate models while adding some level of interpretation to applied problems to act in the prediction of time series. Learning is achieved through extreme learning machines to estimate the parameters and a technique of selection of characteristics using regularization concept and resampling, which is able to perform the definition of the network topology through the selection of subsets of fuzzy neuron more significant to the problem. Numerical experiments are presented for time series problems using benchmark bases on machine learning problems. The results obtained are compared to other techniques of prediction of reference series in the literature. The model made rough estimates of the responses obtained by the models of fuzzy neural networks for time series forecasting with fewer fuzzy rules.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Formal Verification of a Fuzzy Rule-Based Classifier Using the Prototype Verification System
Nächstes Kapitel Design and Implementation of Fuzzy Expert System Based on Evolutionary Algorithms for Diagnosing the Intensity Rate of Hepatitis C
Literatur
1.
Angelov, P.P., Filev, D.P.: An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(1), 484–498 (2004)CrossRef
2.
Bach, F.R.: Bolasso: model consistent lasso estimation through the bootstrap. In: Proceedings of the 25th International Conference on Machine learning, pp. 33–40. ACM, July 2008
3.
Bordignon, F.L.: Aprendizado extremo para redes neurais fuzzy baseadas em uninormas (2013)
4.
Bordignon, F., Gomide, F.: Uninorm based evolving neural networks and approximation capabilities. Neurocomputing 127, 13–20 (2014)CrossRef
5.
Box, G.E., Box, G.M.J., Gregory, C.R.: Time series analysis: forecasting and control, No. 04, QA280, B6 1994 (1994)
6.
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Ann. Stat. 32(2), 407–499 (2004)MathSciNetCrossRef
7.
8.
Hell, M.B.: Abordagem neurofuzzy para modelagem de sistemas dinamicos não lineares. Doctoral dissertation, Faculdade de Engenharia Elétrica e de Computação, Universidade Estadual de Campinas (UNICAMP) (2008)
9.
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)CrossRef
10.
Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)CrossRef
11.
Kasabov, N.K., Song, Q.: DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans. Fuzzy Syst. 10(2), 144–154 (2002)CrossRef
12.
Leite, D., Gomide, F., Ballini, R., Costa, P.: Fuzzy granular evolving modeling for time series prediction. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 2794–2801. IEEE, June 2011
13.
Lemos, A., Caminhas, W., Gomide, F.: New uninorm-based neuron model and fuzzy neural networks. In: 2010 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1–6. IEEE, July 2010
14.
Lemos, A.P., Caminhas, W., Gomide, F.: A fast learning algorithm for uninorm-based fuzzy neural networks. In: 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1–6. IEEE, August 2012
15.
Murphy, K.P.: Machine learning: a probabilistic perspective. MIT Press, Cambridge (2012)MATH
16.
17.
Rosa, R., Gomide, F., Ballini, R.: Evolving hybrid neural fuzzy network for system modeling and time series forecasting. In: 2013 12th International Conference on Machine Learning and Applications (ICMLA), vol. 2, pp. 378–383. IEEE, December 2013
18.
Souza, P.V.C., Lemos, A.P.: Redes Neurais Nebulosas para problemas de Classificação. In: Simpósio Brasileiro de Automação Inteligente, Natal-RN. XII SBAI-Simpósio Brasileiro de Automação Inteligente (2015)
19.
20.
Souza, P.V.C.: Regularized fuzzy neural networks for pattern classification problems. Int. J. Appl. Eng. Res. 13(5), 2985–2991 (2018)
21.
Dash, P.K., Ramakrishna, G., Liew, A.C., Rahman, S.: Fuzzy neural networks for time-series forecasting of electric load. IEE Proc.-Gener. Transm. Distrib. 142(5), 535–544 (1995)CrossRef
22.
Maguire, L.P., Roche, B., McGinnity, T.M., McDaid, L.J.: Predicting a chaotic time series using a fuzzy neural network. Inf. Sci. 112(1–4), 125–136 (1998)CrossRef
23.
Braga, A.D.P., Carvalho, A.P.L.F., Ludermir, T.B.: Redes neurais artificiais: teoria e aplicações, pp. 5–55. Livros Técnicos e Científicos (2000)
24.
Metadaten
Titel
Regularized Fuzzy Neural Network Based on Or Neuron for Time Series Forecasting
verfasst von
Paulo Vitor de Campos Souza
Luiz Carlos Bambirra Torres
Copyright-Jahr
2018
Buch
Fuzzy Information Processing

Print ISBN: 978-3-319-95311-3

Electronic ISBN: 978-3-319-95312-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-95312-0_2

Premium Partner

    Bildnachweise