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Erschienen in: Soft Computing 12/2015

23.04.2014 | Focus

Fuzzy slopes model of nonlinear systems with sparse data

verfasst von: José de Jesús Rubio

Erschienen in: Soft Computing | Ausgabe 12/2015

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Abstract

In this paper, a fuzzy slopes model is introduced for the modeling of nonlinear systems with sparse data. The proposed method is the combination of the slopes and fuzzy models. The slopes model is used to estimate the missing output data of a nonlinear behavior; later, the fuzzy model is used to learn this behavior. The proposed method avoids the requirement to know all the data. The output of the slopes algorithm is guaranteed to be bounded. The experiments show the effectiveness of the proposed technique.

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Literatur
Zurück zum Zitat Aguilar-Lopez R, Mata-Machuca JL, Martinez-Guerra R (2012) Temperature control of continuous chemical reactors under noisy measurements and model uncertainties. J Appl Res Technol (JART) 10(3):428–446 Aguilar-Lopez R, Mata-Machuca JL, Martinez-Guerra R (2012) Temperature control of continuous chemical reactors under noisy measurements and model uncertainties. J Appl Res Technol (JART) 10(3):428–446
Zurück zum Zitat Aguilar-Lopez R, Martinez-Guerra R, Puebla H, Hernandez-Suarez R (2010) High order sliding-mode dynamic control for chaotic intracellular calcium oscillations. Nonlinear Anal B Real World Appl 11:217–231MATHMathSciNetCrossRef Aguilar-Lopez R, Martinez-Guerra R, Puebla H, Hernandez-Suarez R (2010) High order sliding-mode dynamic control for chaotic intracellular calcium oscillations. Nonlinear Anal B Real World Appl 11:217–231MATHMathSciNetCrossRef
Zurück zum Zitat Balaguer-Ballester E, Bouchachia H, Lapish CC (2013) Identifying sources of non-stationary neural ensemble dynamics. BMC Neurosci 14(Suppl 1):15 Balaguer-Ballester E, Bouchachia H, Lapish CC (2013) Identifying sources of non-stationary neural ensemble dynamics. BMC Neurosci 14(Suppl 1):15
Zurück zum Zitat Bordignon F, Gomide F (2014) Uninorm based evolving neural networks and approximation capabilities. Neurocomputing 127:13–20CrossRef Bordignon F, Gomide F (2014) Uninorm based evolving neural networks and approximation capabilities. Neurocomputing 127:13–20CrossRef
Zurück zum Zitat Brodka P, Saganowski S, Kazienko P (2013) GED: the method for group evolution discovery in social networks. Soc Netw Anal Min 3:1–14CrossRef Brodka P, Saganowski S, Kazienko P (2013) GED: the method for group evolution discovery in social networks. Soc Netw Anal Min 3:1–14CrossRef
Zurück zum Zitat Cruz-Vega I, Yu W (2010) Multiple fuzzy neural networks modeling with sparse data. Neurocomput 73:2446–2453CrossRef Cruz-Vega I, Yu W (2010) Multiple fuzzy neural networks modeling with sparse data. Neurocomput 73:2446–2453CrossRef
Zurück zum Zitat García-Cuesta E, Iglesias JA (2012) User modeling: through statistical analysis and subspace learning. Expert Syst Appl 39(5):5243–5250CrossRef García-Cuesta E, Iglesias JA (2012) User modeling: through statistical analysis and subspace learning. Expert Syst Appl 39(5):5243–5250CrossRef
Zurück zum Zitat Jang JSR, Sun CT (1996) Neuro-fuzzy and soft computing. Prentice Hall, Englewood Cliffs 07458 Jang JSR, Sun CT (1996) Neuro-fuzzy and soft computing. Prentice Hall, Englewood Cliffs 07458
Zurück zum Zitat Lughofer E (2012) Sigle pass active learning with conflict and ignorance. Evol Syst 3:251–271CrossRef Lughofer E (2012) Sigle pass active learning with conflict and ignorance. Evol Syst 3:251–271CrossRef
Zurück zum Zitat Lughofer E, Trawinski B, Trawinski K, Kempa O, Lasota T (2011) On employing fuzzy modeling algorithms for the valuation of residential premises. Inf Sci 181:5123–5142CrossRef Lughofer E, Trawinski B, Trawinski K, Kempa O, Lasota T (2011) On employing fuzzy modeling algorithms for the valuation of residential premises. Inf Sci 181:5123–5142CrossRef
Zurück zum Zitat Lughofer E (2011) Evolving fuzzy systems—methodologies. Advanced concepts and applications. Springer, Berlin HeidelbergMATHCrossRef Lughofer E (2011) Evolving fuzzy systems—methodologies. Advanced concepts and applications. Springer, Berlin HeidelbergMATHCrossRef
Zurück zum Zitat Maciel L, Lemos A, Gomide F, Ballini R (2012) Evolving fuzzy systems for pricing fixed income options. Evol Syst 3:5–18CrossRef Maciel L, Lemos A, Gomide F, Ballini R (2012) Evolving fuzzy systems for pricing fixed income options. Evol Syst 3:5–18CrossRef
Zurück zum Zitat Marques Silva A, Caminhas W, Lemos A, Gomide F (2014) A fast learning algorithm for evolving neo-fuzzy neuron. Appl Soft Comput 14(B):194–209CrossRef Marques Silva A, Caminhas W, Lemos A, Gomide F (2014) A fast learning algorithm for evolving neo-fuzzy neuron. Appl Soft Comput 14(B):194–209CrossRef
Zurück zum Zitat Musiał K, Kazienko P (2013) Social networks on the Internet. World Wide Web 16:31–72CrossRef Musiał K, Kazienko P (2013) Social networks on the Internet. World Wide Web 16:31–72CrossRef
Zurück zum Zitat Perez-Cruz JH, Rubio JJ, Pacheco J, Soriano E (2014) State estimation in MIMO nonlinear systems subject to unknown dead zones using recurrent neural networks. Neural Comput Appl. doi:10.1007/s00521-013-1533-5 Perez-Cruz JH, Rubio JJ, Pacheco J, Soriano E (2014) State estimation in MIMO nonlinear systems subject to unknown dead zones using recurrent neural networks. Neural Comput Appl. doi:10.​1007/​s00521-013-1533-5
Zurück zum Zitat Perez-Cruz JH, Chairez I, Rubio JJ, Pacheco J (2014) Identification and control of a class of nonlinear systems with nonsymmetric deadzone using recurrent neural networks. IET Control Theory Appl 8(3):183–192MathSciNetCrossRef Perez-Cruz JH, Chairez I, Rubio JJ, Pacheco J (2014) Identification and control of a class of nonlinear systems with nonsymmetric deadzone using recurrent neural networks. IET Control Theory Appl 8(3):183–192MathSciNetCrossRef
Zurück zum Zitat Pratama M, Anavatti SG, Angelov PP, Lughofer E (2014) PANFIS: a novel incremental learning machine. IEEE Trans Neural Netw Learn Syst 25(1):55–68CrossRef Pratama M, Anavatti SG, Angelov PP, Lughofer E (2014) PANFIS: a novel incremental learning machine. IEEE Trans Neural Netw Learn Syst 25(1):55–68CrossRef
Zurück zum Zitat Rubio JJ, Vázquez DM, Mújica-Vargas D (2013) Acquisition system and approximation of brain signals. IET Sci Meas Technol 7(4):232–239CrossRef Rubio JJ, Vázquez DM, Mújica-Vargas D (2013) Acquisition system and approximation of brain signals. IET Sci Meas Technol 7(4):232–239CrossRef
Zurück zum Zitat Rubio JJ (2014) Evolving intelligent algorithms for the modelling of brain and eye signals. Appl Soft Comput 14(B):259–268CrossRef Rubio JJ (2014) Evolving intelligent algorithms for the modelling of brain and eye signals. Appl Soft Comput 14(B):259–268CrossRef
Zurück zum Zitat Rubio JJ, Perez-Cruz JH (2014) Evolving intelligent system for the modelling of nonlinear systems with dead-zone input. Appl Soft Comput 14(B):289–304CrossRef Rubio JJ, Perez-Cruz JH (2014) Evolving intelligent system for the modelling of nonlinear systems with dead-zone input. Appl Soft Comput 14(B):289–304CrossRef
Zurück zum Zitat Rubio JJ, Soriano LA, Yu W (2014) Dynamic model of a wind turbine for the electric energy generation. Math Probl Eng 2014:1–8 Rubio JJ, Soriano LA, Yu W (2014) Dynamic model of a wind turbine for the electric energy generation. Math Probl Eng 2014:1–8
Zurück zum Zitat Soriano LA, Yu W, Rubio JJ (2013) Modeling and control of wind turbine. Math Probl Eng 2013:1–13CrossRef Soriano LA, Yu W, Rubio JJ (2013) Modeling and control of wind turbine. Math Probl Eng 2013:1–13CrossRef
Zurück zum Zitat Trawinski B (2013) Evolutionary fuzzy system ensemble approach to model real estate market based on data stream exploration. J Univ Comput Sci 19(4):539–562MathSciNet Trawinski B (2013) Evolutionary fuzzy system ensemble approach to model real estate market based on data stream exploration. J Univ Comput Sci 19(4):539–562MathSciNet
Zurück zum Zitat Vázquez DM, Rubio JJ, Pacheco J (2012) Characterization framework for epileptic signals. IET Image Process 6(9):1227–1235MathSciNetCrossRef Vázquez DM, Rubio JJ, Pacheco J (2012) Characterization framework for epileptic signals. IET Image Process 6(9):1227–1235MathSciNetCrossRef
Zurück zum Zitat Wang LX (1997) A course in fuzzy systems and control. ISBN:0-13-540882-2 Wang LX (1997) A course in fuzzy systems and control. ISBN:0-13-540882-2
Metadaten
Titel
Fuzzy slopes model of nonlinear systems with sparse data
verfasst von
José de Jesús Rubio
Publikationsdatum
23.04.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 12/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-014-1289-6

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