Recursive Gath-Geva clustering as a basis for evolving neuro-fuzzy modeling

Evolving Systems

Volumn 1, Issue 1, 2010, Pages 59-71

  Soleimani B, Hossein ^a   Lucas, Caro ^a,b   Araabi, Babak N ^a,b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b INSTITUTE FOR RESEARCH IN FUNDAMENTAL SCIENCES IPM   (Iran)

Author keywords

Evolving neuro fuzzy model (ENFM); Modeling time varying systems; Online adaptive learning; Recursive Gath Geva clustering; Time series prediction

Indexed keywords

ADAPTIVE LEARNING; MODELING TIME VARYING SYSTEMS; NEURO-FUZZY MODEL; RECURSIVE GATH-GEVA CLUSTERING; TIME SERIES PREDICTION;

CLUSTERING ALGORITHMS; COMPUTER SIMULATION; COVARIANCE MATRIX; FORECASTING; ONLINE SYSTEMS; TIME SERIES; TIME VARYING SYSTEMS;

E-LEARNING;

EID: 79952317714     PISSN: 18686478     EISSN: 18686486     Source Type: Journal    
DOI: 10.1007/s12530-010-9006-x     Document Type: Article

References (34)

1. Angelov PP, Filev DP (2004) An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans Syst Man Cybern B 34(1):484-498.
2. Angelov PP, Filev DP (2005) Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models. In: Proceedings of IEEE Int Conf on Fuzzy Syst, pp 1068-1073.
3. Angelov PP, Zhou X (2006) Evolving fuzzy systems from data streams in real-time. In: Proceedings of IEEE Int Symp on Evolving Fuzzy Systems, pp 29-35.
4. Beringer J, Hullermeier E (2008) Online clustering of parallel data streams. Data Knowl Eng 58:180-204.
5. Bittanti S, Piroddi L (1996) Nonlinear identification and control of a heat exchanger: a neural network approach. J Franklin Inst 334(1):135-153.
6. Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267-278.
7. Crespo F, Weber R (2005) A methodology for dynamic data mining based on fuzzy clustering. Fuzzy Sets Syst 150:267-284.
8. DaISy (2009) A database for identification of systems. http://www. esat.kuleuven.be/sista/daisy/.
9. Deng D, Kasabov N (2000) Evolving self-organizing maps for online learning, data analysis and modeling. In: Proceedings of IJCNN 2000 neural networks, neural computation: new challenges perspectives new millennium, Como, Italy, pp 3-8.
10. Fritzke B (1995) A growing neural gas network learns topologies. Adv Neural Inf Process Syst 7:625-632.
11. Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 11(7):773-780.
12. Georgieva O, Klawonn F (2008) Dynamic data assigning assessment clustering of streaming data. Appl Soft Comput 8:1305-1313.
13. Gholipour A, Araabi BN, Lucas C (2006) Predicting chaotic time series using neural and neurofuzzy models: a comparison study. Neural Process Lett 24:217-239.
14. Gustafson DE, Kessel WC (1978) Fuzzy clustering with a fuzzy covariance matrix. In: Proceedings of IEEE Conf on decision and control, San Diego, pp 761-766.
15. Hoppner F, Klawonn F et al (1999) Fuzzy cluster analysis. Wiley, London.
16. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665-685.
17. Kasabov N (1998) Evolving fuzzy neural networks-algorithms, applications and biological motivation. In: Yamakawa T, Matsumoto G (eds) Methodologies for the conception, design and applications of soft computing. World Scientific, Singapore, pp 271-274.
18. Kasabov N, Song Q (2002) DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst 10(2):144-154.
19. Klim CH, Kim MS, Lee JJ (2007) Incremental hyperplane-based fuzzy clustering for system modeling. In: Proceedings of 33rd Conf of IEEE Industrial Electronics Society, Taipei, Taiwan, pp 614-619.
20. Lughofer E, Klement EP (2005) FLEXFIS: a variant for incremental learning of Takagi-Sugeno fuzzy systems. In: Proceedings of IEEE Int Conf on fuzzy systems, pp 915-920.
21. Lughofer ED (2008) FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi-Sugeno fuzzy models. IEEE Trans Fuzzy Syst 16(6):1393-1410.
22. Mackey M, Glass L (1977) Oscillation and chaos in psychological control systems. Science 197:281-287.
23. Martinez B, Herrera F, Fernandez J, Marichal E (2008) An incremental clustering method and its application in online fuzzy modeling. Stud Fuzziness Soft Comput 224:163-178.
24. McNish AG, Lincoln JV (1949) Prediction of sunspot numbers. Trans Am Geophys Union 30:673.
25. Mirmomeni M, Lucas C, Araabi BN (2009) Introducing recursive learning algorithm for system identification of nonlinear time varying processes. In: Proceedings of 17th Mediterranean Conf. on Control and Automation, Thessaloniki, Greece, pp 736-741.
26. Nelles O (1996) Local linear model tree for on-line identification of time invariant nonlinear dynamic systems. In: Proceedings of Int Conf on Artificial Neural Networks (ICANN), Bochum, Germany, pp 115-120.
27. Nelles O (2001) Nonlinear system identification. Springer, London.
28. Palit AK, Popovic D (2005) Computational intelligence in time series forecasting: theory and engineering applications. Advances in industrial control. Springer, London.
29. Pedrycz W (2008) A dynamic data granulation through adjustable fuzzy clustering. Pattern Recognit Lett 29:2059-2066.
30. Platt J (1991) A resource allocation network for function interpolation. Neural Comput 3:213-225.
31. Sello S (2001) Solar cycle forecasting: a nonlinear dynamic approach. Astron Astrophys 377(1):312-320.
32. SIDC (2003) Solar influences data analysis center. http://sidc.oma.be/index.php3.
33. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans Syst Man Cybern 15:116-132.
34. Zilouchian A, Jamshidi M (eds) (2001) Intelligent control systems using soft computing methodologies. CRC Press, Boca Raton.