Modal description of vibratory behaviour of structures using fuzzy membership functions

Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics

Volumn 218, Issue 4, 2004, Pages 173-181

  Khoshnoud, Farbod ^a   Khoshnoud, Farhoud ^a   Esat, I I ^a  

^a BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

Frequency response functions; Fuzzy membership functions; Modal analysis

Indexed keywords

BOUNDARY CONDITIONS; DATA REDUCTION; DEGREES OF FREEDOM (MECHANICS); FREQUENCY RESPONSE; INTERPOLATION; NATURAL FREQUENCIES; NEURAL NETWORKS; VIBRATIONS (MECHANICAL);

FREQUENCY RESPONSE FUNCTIONS; FUNCTION PARAMETERS; FUZZY MEMBERSHIP FUCTIONS;

FUZZY SETS;

EID: 10644283876     PISSN: 14644193     EISSN: None     Source Type: Journal    
DOI: 10.1243/1464419043541473     Document Type: Conference Paper

References (17)

1. Vafaei, S., Rahnejat, H. and Ainl, R. Vibration monitoring of high speed spindles using spectral analysis techniques. Int. J. Machine Tools Manuf., 2002, 42, 1223-1234.
2. Budak, E. and Altintas, Y. Analitical prediction of chatter stability in milling - part II: application of general formulation to common milling systems. ASME J. Dynamic Syst. Measmt Control, 1998, 120, 31-36.
3. Esat, I. I. and Bahai, H. Vibratory system synthesis for multi-body systems based on genetic algorithm. J. Sound Vibr., 2000, 230, 933-948.
4. Chassiakos, A. G. and Masri, S. F. Identification of structural systems by neural networks. Math. Comput. Simul., 1996, 40, 637-656.
5. Wasfy, T. M. and Noor, Ahmed K. Application of fuzzy sets to transient analysis of space structures, Finite Elements Anal. Des., 1998, 29, 153-171.
6. Li, Q. S., Liu, D. K., Fang, J. Q., Jeary, A. P. and Wong, C. K. Damping in buildings: its neural network model and AR model. Eng. Struct., 2000, 22, 1216-1223.
7. Warnock, T. W. and Cochran, J. E. Predicting the orbital lifetime of tethered satellite systems. Acta Astronaut., 1995, 35, 193-203.
8. Li, S. and Elbestawi, M. A. Tool condition monitoring in machining by fuzzy neural networks. ASME J. Dynamic Syst., Measmt Control, 1994, 116, 567-576.
9. Bahar, H. B. and Horrocks, D. H. Dynamic weight estimation using an artificial neural network. Artif. Intelli. Engng, 1998, 12, 135-139.
10. Jin, L., Nikiforuk, P. N. and Gupta, M. M. Dynamic recurrent neural networks for control on unknown nonlinear systems. ASME J. Dynamic Syst. Measmt Control, 1994, 116, 567-576.
11. Park, S. S., Altintas, Y. and Movahhedy, M. Receptance coupling for end mills. Int. J. Machine Tools Manuf., 2003, 43, 889-896.
12. Tam, C. K. W. and Kurbatskii, K. A. A wavenumber-based extrapolation and interpolation method for use in conjunction with high-order finite difference schemes. J. Comput. Phys., 2000, 157, 588-617.
13. Patel, M. H. A linear program to detect extrapolation in predicting new responses of a multiple linear regression model. Comput. Indust. Engng, 1995, 28, 787-791.
14. Narayana, S., Sarkar, T. K. S., Adve, R., Wicks, M. and Vannicola, V. A comparison of two techniques for the interpolation/extrapolation of frequency domain responses. Digital Signal Process., 1996, 6, 51-67.
15. Garbey, M. and Shyy, W. A least square extrapolation method for improving solution accuracy of PDE computations. J. Comput. Phys., 2003, 186, 1-23.
16. Sun, W. and Davidson, V. J. Dynamic fuzzy-reasoning-based function estimator. Fuzzy Sets Syste., 1996, 79, 357-366.
17. Blevins, R. D. Formulas for Natural Frequency and Mode Shape, 1990 (Van Nostrand Reinhold, New York).