Architectural effect on anfis for machine condition assessment

American Society of Mechanical Engineers, Dynamic Systems and Control Division (Publication) DSC

Volumn 73, Issue 1 PART A, 2004, Pages 663-670

  Sheng, Shuangwen ^a   Gao, Robert X ^a  

^a University of Massachusetts   (United States)

Author keywords

ANFIS architecture; Defect severity classification; Dynamic load estimation; MF number; MF type

Indexed keywords

DYNAMIC LOADS; ERROR ANALYSIS; FUZZY CONTROL; MATHEMATICAL MODELS; NEURAL NETWORKS; OPTIMIZATION; REGRESSION ANALYSIS; TREES (MATHEMATICS);

ANFIS ARCHITECTURE; DEFECT SEVERITY CLASSIFICATION; DYNAMIC LOAD ESTIMATION; MF NUMBER; MF TYPE;

ADAPTIVE SYSTEMS;

EID: 19644396604     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1115/IMECE2004-60071     Document Type: Conference Paper

References (14)

1. J. Jang, C. Sun, and E. Mizutani. 1997, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, New Jersey.
2. Zadeh, L. A., 1983, "The role of fuzzy-Logic in the management of uncertainty in expert systems," Fuzzy Sets and Systems, 11(3), pp. 199-227.
3. Yen, G. and Meesad, P., 2001, "An effective neuro-fuzzy paradigm for machineiy condition health monitoring," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 31(4), pp. 523-536.
4. Uebele, V., Abe, S., and Lan, M. S., 1995, "A neuralnetwork-based fuzzy classifier," IEEE Transactions on Systems Man and Cybernetics, 25(2), pp. 353-361.
5. Jang, J.-S. R., 1993, "ANFIS: Adaptive-Network-Based Fuzzy Inference System," IEEE Transactions on Systems, Man and Cybernetics, 23(3). pp. 665-685.
6. Djukanovic, M B., Calovic, M. S.. Vesovic, B. V., and Sobajic, D. J., 1997,"Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system," IEEE Transactions on Energy Conversion, 12(4), pp. 375-381.
7. Breimaa L., Friedman, J. H., Olshen, R. A., and Stone, C. J., 1984, Classification and regression trees, Wadsworth. Inc., Belmont, California.
8. Bezdek, J., 1981, Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.
9. Rojas, I., Beraier, J. L., Rodriguez-Alvarez, M., and Prieto, A., 2000,"What are the main functional block involved in the design of Adaptive Neuro-Fuzzy Inference Systems?," Proceedings of the International Joint Conference on Neural Networks, 6, pp. 551-556.
10. Kreinovich, V., Quintana, C., and Reznik, L., 1992, "Gaussian membership functions are most adequate in representing uncertainty in measurements," NAFIPS '92, 11, pp. 618-624.
11. Pedrycz, W., 1994, "Why triangular membership functions," Fuzzy Sets and Systems, 64(1), pp. 21-30.
12. Altmann, J., and Mathew, J., 2001, "Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis," Mechanical Systems and Signal Processing, 15(5), pp. 963-977.
13. The Mathworks Inc., 2000, Fuzzy Logic Toolbox User's Guide, Version 2.
14. Mitaim, S. and Kosko, B., 2001, "The shape of fuzzy sets in adaptive function approximation," IEEE Transactions on Fuzzy Systems, 9(4), pp. 637-656.