nach oben

Soft Computing

Erschienen in:

19.11.2015 | Methodologies and Application

MSAFIS: an evolving fuzzy inference system

verfasst von: José de Jesús Rubio, Abdelhamid Bouchachia

Erschienen in: Soft Computing | Ausgabe 9/2017

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, the problem of learning in big data is considered. To solve this problem, a new algorithm is proposed as the combination of two important evolving and stable intelligent algorithms: the sequential adaptive fuzzy inference system (SAFIS), and stable gradient descent algorithm (SGD). The modified sequential adaptive fuzzy inference system (MSAFIS) is the SAFIS with the difference that the SGD is used instead of the Kalman filter for the updating of parameters. The SGD improves the Kalman filter, because it first obtains a better learning in big data. The effectiveness of the introduced method is verified by two experiments.

Vorheriger Artikel Marginal patch alignment for dimensionality reduction

Nächster Artikel A method of combining forward with backward greedy algorithms for sparse approximation to KMSE

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 "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

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

Ahn CK (2012) An error passivation approach to filtering for switched neural networks with noise disturbance. Neural Comput Appl 21(5):853–861CrossRef

Ahn CK (2014) A new solution to the induced l\(\infty \) finite impulse response filtering problem based on two matrix inequalities. Int J Control 87(2):404–409MathSciNetCrossRefMATH

Ahn CK, Lim MT (2013) Model predictive stabilizer for T-S fuzzy recurrent multilayer neural network models with general terminal weighting matrix. Neural Comput Appl 23(Suppl 1):S271–S277CrossRef

Angelov P, Filev D, Kasabov N (2010) Evolving intelligent systems—methodology and applications. Wiley, New YorkCrossRef

Bordignon F, Gomide F (2014) Uninorm based evolving neural networks and approximation capabilities. Neurocomputing 127:13–20CrossRef

Bouchachia A (2008) Incremental Learning. Encyclopedia of Data Warehousing and Mining, pp 1006–1012

Bouchachia A (2014) Online dataprocessing. Neurocomputing 126:116–117CrossRef

Bouchachia A, Lena A, Vanaret C (2014) Online and interactive self-adaptive learning of user profile using incremental evolutionary algorithms. Evol Syst 5:143–157CrossRef

Bouchachia A, Vanaret C (2014) GT2FC: an online growing interval type-2 self-learning fuzzy classifier. IEEE Trans Fuzzy Syst 22(4):999–1018CrossRef

Gama J (2010) Knowledge discovery from data streams. Chapman & Hall/CRC, Boca RatonCrossRefMATH

Gama J, Zliobaite I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv 46(4):44CrossRefMATH

Garcia-Cuesta E, Iglesias JA (2012) User modeling: Through statistical analysis and subspace learning. Expert Syst Appl 39:5243–5250CrossRef

Gomide F, Lughofer E (2014) Recent advances on evolving intelligent systems and applications. Evolv Syst 5:217–218CrossRef

Hartert L, Sayed-Mouchaweh M (2014) Dynamic supervised classification method for online monitoring in non-stationary environments. Neurocomputing 126:118–131CrossRef

Huang G-B, Saratchandran P, Sundararajan N (2004) An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. IEEE Trans Syst Man Cybern Part B Cybern 34(6):2284–2292

Iglesias JA, Tiemblo A, Ledezma A, Sanchis A (2015) Web news mining in an evolving framework. Inf Fusion. doi:10.1016/j.inffus.2015.07.004

Iglesias JA, Ledezma A, Sanchis A (2014) Evolving classification of UNIX users’ behaviors. Evolv Syst 5:231–238CrossRef

Iglesias JA, Skrjanc I (2014) Applications, results and future direction. Evolv Syst 5:1–2CrossRef

Kasabov N (2007) Evolving connectionist systems: the knowledge engineering approach, 2nd edn. Springer Verlag, LondonMATH

Klancar G, Skrjanc I (2015) Evolving principal component clustering with a low run-timecomplexity for LRF data mapping. Appl Soft Comput 35:349–358

Lughofer E (2011) Evolving fuzzy systems—methodologies, advanced concepts and applications. Springer, Berlin, HeidelbergCrossRefMATH

Lughofer E, Cernuda C, Kindermann S, Pratama M (2015) Generalized smart evolving fuzzy systems. Evolv Syst. doi:10.1007/s12530-015-9132-6

Lughofer E, Sayed-Mouchaweh M (2015) Autonomous data stream clustering implementing split-and-merge concepts—towards a plug-and-play approach. Inf Sci 304:54–79CrossRef

Lughofer E, Sayed-Mouchaweh M (2015) Adaptive and on-line learning in non-stationary environments. Evol Syst 6:75–77CrossRef

Marques Silva A, Caminhas W, Lemos A, Gomide F (2014) A fast learning algorithm for evolving neo-fuzzy neuron. Appl Soft Comput 14:194–209CrossRef

Ordoñez FJ, Iglesias JA, de Toledo P, Ledezma A, Sanchis A (2013) Online activity recognition using evolving classifiers. Expert Syst Appl 40:1248–1255CrossRef

Perez-Cruz JH, Rubio JJ, Pacheco J, Soriano E (2014) State estimation in MIMO nonlinear systems subject to unknown deadzones using recurrent neural networks. Neural Comput Appl 25(3–4):693–701CrossRef

Perez-Cruz JH, Rubio JJ, Encinas R, Balcazar R (2014) Singularity-free neural control for the exponential trajectory tracking in multiple-input uncertain systems with unknown deadzone nonlinearities. The Scientific World Journal 2014:1–10CrossRef

Pratama M, Anavatti SG, Er MJ, Lughofer ED (2015) pClass: an effective classifier for streaming examples. IEEE Trans Fuzzy Syst 23(2):369–386CrossRef

Precup R-E, Sabau M-C, Petriu EM (2015) Nature-inspired optimal tuning of input membership functions ofTakagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems. Appl Soft Comput 27:575–589CrossRef

Rong HJ, Sundararajan N, Huang GB, Saratchandran P (2006) Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets Syst 157(9):1260–1275MathSciNetCrossRefMATH

Rubio JJ, Angelov P, Pacheco J (2011) An uniformly stable backpropagation algorithm to train a feedforward neural network. IEEE Trans Neural Netw 22(3):356–366CrossRef

Rubio JJ, Ortiz F, Mariaca CR, Tovar JC (2013) A method for online pattern recognition for abnormal eye movements. Neural Comput Appl 22(3–4):597–605CrossRef

Rubio JJ, Vazquez DM, Mujica-Vargas D (2013) Acquisition system and approximation of brain signals. IET Sci Meas Technol 7(4):232–239CrossRef

Sayed-Mouchaweh M, Lughofer E (2015) Decentralized fault diagnosis approach without a global model for fault diagnosis of discrete event systems. Int J Control. doi:10.1080/00207179.2015.1039594

Sayed-Mouchaweh M, Lughofer E (2012) Learning in non-stationary environments: methods and applications. Springer, New YorkCrossRefMATH

Torres C, Rubio JJ, Aguilar-Ibañez C, Perez-Cruz JH (2014) Stable optimal control applied to a cylindrical robotic arm. Neural Comput Appl 24(3–4):937–944CrossRef

Zdesar A, Dovzan D, Skrjanc I (2014) Self-tuning of 2 DOF control based on evolving fuzzy model. Appl Soft Comput 19:403–418CrossRef

Titel: MSAFIS: an evolving fuzzy inference system
verfasst von: José de Jesús Rubio
Abdelhamid Bouchachia
Publikationsdatum: 19.11.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 9/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-015-1946-4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 9/2017

A unified framework for the key weights in MAGDM under uncertainty

Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems

A direct projection-based group decision-making methodology with crisp values and interval data

Classification with boosting of extreme learning machine over arbitrarily partitioned data

On the category of rough sets

A solid transportation model with product blending and parameters as rough variables

Premium Partner