An evolving fuzzy predictor for industrial applications

IEEE Transactions on Fuzzy Systems

Volumn 16, Issue 6, 2008, Pages 1439-1449

  Wang, Wilson ^a   Vrbanek Jr , Josip ^a  

^a LAKEHEAD UNIVERSITY   (Canada)

Author keywords

Adaptive training; Evolving fuzzy system (EFS); Machinery condition monitoring; Material property testing; Multistep prediction; Recursive Levenberg Marquardt (R LM)

Indexed keywords

BENCHMARKING; CLUSTERING ALGORITHMS; CONDITION MONITORING; FORECASTING; FUZZY SYSTEMS; INDUSTRIAL APPLICATIONS; LAGRANGE MULTIPLIERS; MACHINERY; ONLINE SYSTEMS; RECURSIVE FUNCTIONS;

ADAPTIVE TRAINING; EVOLVING FUZZY SYSTEM (EFS); MACHINERY CONDITION MONITORING; MATERIAL PROPERTY TESTING; MULTISTEP PREDICTION; RECURSIVE LEVENBERG-MARQUARDT (R-LM);

FUZZY LOGIC;

EID: 58149502268     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2008.925918     Document Type: Article

References (25)

1. M. Pourahmadi, Foundation of Time Series Analysis and Prediction Theory. New York: Wiley, 2001.
2. D. Chelidze and J. Cusumano, "A dynamical systems approach to failure prognosis," J. Vib. Acoust., vol. 126, pp. 1-7, 2004.
3. C. Li and H. Lee, "Gear fatigue crack prognosis using embedded model, gear dynamicmodel and fracture mechanics," Mech. Syst. Signal Process., vol. 9, pp. 836-846, 2005.
4. M. Figueiredo, R. Ballini, S. Soares, M. Andrade, and F. Gomide, "Learning algorithms for a class of neurofuzzy network and applications," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 34, no. 3, pp. 293-301, Aug. 2004.
5. Y. Liang and X. Liang, "Improving signal prediction performance of neural networks through multiresoltion learning approach," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 36, no. 2, pp. 341-352, Apr. 2006.
6. H. Ishibuchi and T. Yamamoto, "Rule weight specification in fuzzy rule-based classification systems," IEEE Trans. Fuzzy Syst., vol. 13, no. 4, pp. 428-435, Aug. 2005.
7. W. Wang, F. Golnaraghi, and F. Ismail, "Prognosis of machine health condition using neuro-fuzzy systems," Mech. Syst. Signal Process. vol. 18, no. 4, pp. 813-831, 2004.
8. W. Wang, "An intelligent system for machinery condition monitoring," IEEE Trans. Fuzzy Syst., vol. 16, no. 1, pp. 110-122, Feb. 2008.
9. W.Wang, F. Ismail, and F. Golnaraghi, "A neuro-fuzzy approach for gear system monitoring," IEEE Trans. Fuzzy Syst., vol. 16, no. 3, pp. 710-723, Jun. 2004.
10. W. Wang, F. Golnaraghi, and F. Ismail, "Condition monitoring of a multistage printing press," J. Sound Vib., vol. 270, pp. 755-766, 2004.
11. P. Angelov, Evolving Rule Based Models: A Tool for Design of Flexible Adaptive Systems. Heidelberg, Germany: Physica-Verlag, 2002.
12. N. Kasabov and D. Filev, "Evolving intelligent systems: Methods, learning, and applications," in Proc. 2nd Int. Symp. Evolving Fuzzy Syst., London, U.K., IEEE Press, 2006, pp. 8-18.
13. L. Wang, "Fuzzy systems are universal approximaters," in Proc. FUZZIEEE, San Diego, CA, 1992, pp. 1163-1170.
14. P. Angelov and D. Filev, "An approach to online identification of evolving Takagi-Sugeno models," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 1, pp. 484-498, Feb. 2004.
15. P. Angelov and R. Ruswell, "Identification of evolving rule-based models," IEEE Trans. Fuzzy Syst., vol. 10, no. 5, pp. 667-677, Oct. 2002.
16. P. Angelov and X. Zhou, "Evolving fuzzy systems from data streams in real-time," in Proc. 2nd Int. Symp. Evolving Fuzzy Syst., London, U.K.: IEEE Press, 2006, pp. 29-35.
17. N. Kasabov and Q. Song, "DENFIS: Dynamic, evolving neural-fuzzy inference systems and its application for time-series prediction," IEEE Trans. Fuzzy Syst., vol. 10, no. 2, pp. 144-154, Apr. 2002.
18. Q. Song andN.Kasabov, "TWNFI - A transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling," Neural Netw., vol. 19, pp. 1591-1596, 2006.
19. N. Kasabov, "Adaptation and interaction in dynamic systems: Modeling and rule discovery through evolving connectionist systems," Appl. Soft Comput., vol. 6, no. 3, pp. 307-322, 2006.
20. R. Duda, P. Hart, and D. Stork, Pattern Classification, 2nd ed. Oxford, U.K.: Wiley-Interscience, 2000.
21. R. Yager and D. Filev, "Approximate clustering via themountain method," IEEE Trans. Syst., Man Cybern., vol. 24, no. 8, pp. 1279-1284, Aug. 1994.
22. J. Wang andC. Lee, "Self-adaptive neuro-fuzzy inference systems for classification applications," IEEE Trans. Fuzzy Syst., vol. 10, no. 6, pp. 790-802, Dec. 2002.
23. J. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. Syst., Man Cybern., vol. 23, no. 3, pp. 665-885, May/Jun. 1993.
24. M. Mackey and L. Glass, "Oscillation and chaos in physiological control systems," Science, vol. 197, pp. 287-289, 1977.
25. Online Sunspot Data Archive, SIDC, RWC Belgium World Data Center. (2007). [Online]. Available: http://sidc.oma.be/index.php3