Abstract
Fuzzy neural networks display good capacity for self-adaptation and self-learning, and wavelet transformation or analysis reveals time frequency location characteristics and a multi-scale ability. Inspired by these advantages, a new intelligent diagnostic method for machine maintenance, wavelet fuzzy neural network (WFNN), is proposed in this paper. This new intelligent diagnostic method uses wavelet basis function as a membership function whose shape can be adjusted on line so that the networks have better learning and adaptive ability. An on-line learning algorithm is applied to automatically construct the wavelet fuzzy neural network. There are no rules initially in the wavelet fuzzy neural network, they are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The advantages of this learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The results of simulation show that this new intelligent diagnostic method has the advantages of a faster learning rate and higher diagnostic precision.
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References
Kosko, B.: Neural Networks and Fuzzy Systems Đ A Dynamical Systems Approach to Machine Intelligence. Prentice-Hall, Englewood Cliffs
Lee, C.C.: Fuzzy logic in control systems: fuzzy logic controller: part I. IEEE Transactions on Systems, Man, and Cybernetics 20, 404–418 (1990)
Lee, C.C.: Fuzzy logic in control systems: fuzzy logic controller: part II. IEEE Transactions on Systems, Man, and Cybernetics 20, 419–435 (1990)
Liu, P.: Universal approximations of continuous fuzzy-valued functions by multilayer regular fuzzy neural networks. Fuzzy Sets and Systems 119, 313–320 (2001)
Takagi, T., Hayashi, I.: NN-driven fuzzy reasoning. Internat. J. Approx. Reasoning 5, 191–212 (1991)
Jang, J.-S.R.: Fuzzy controller design without domain expert. IEEE Inter. Conf. on Fuzzy Systems, 289–296 (1992)
Wang, L.-X., Mendel, J.M.: Back-propagation fuzzy system as nonlinear dynamic system identifiers. In: IEEE Internat. Conf. on Fuzzy Systems, pp. 1409–1418 (1992)
Shibata, T., Fukuda, T., Kosuge, K., Arai, F.: Skill based control by using fuzzy neural network for hierarchical intelligent control. In: Proc. IJCNN, pp. 81–86 (1992)
Fukuda, T., Shibata, T.: Hierarchical intelligent control for robotic motion by using fuzzy. In: Artificial Intelligence, and Neural Network. Proc. IJCNN 1992, pp. 269–274 (1992)
Nakayama, S., Horikawa, F.S.: T., Uchikawa,Y.: Knowledge acquisition of strategy and tactics using fuzzy neural networks. In: Proc. IJCNN 1992, pp. 751–756 (1992)
Daubechies, I.: The wavelet transform, time–frequency localization, and signal analysis. IEEE Trans. Inform. Theoryl. 36, 961–1005 (1990)
Mallat, S.: A theory for multi-resolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Machine Intell. 11, 674–693 (1989)
Pillay, P., Bhattachariee, A.: Application of wavelets to model short-term power system disturbances. IEEE Trans. Power Syst. 11, 2031–2037 (1996)
Santoso, S., Powers, E.J., Grady, W.M.: Power quality disturbance data compression usingwavelet transform methods. IEEE Trans. Power Delivery 12, 1250–1257 (1997)
Zhang, Q., Benveniste, A.: Wavelet networks. IEEE Trans. on Neural Networks 3, 889–898 (1992)
Thuillard, M.: Applications of wavelets and wavenets in soft computing illustrated with the example of fire detectors. In: SPIE Wavelet Applications VII, Orlando, April 24-28 (2000b) (in press)
Ho, D.W.C., Zhang, P.-A., Xu, J.: Fuzzy Wavelet Networks for Function Learning. IEEE Transactions on Fuzzy Systems 9, 200–211 (2001)
Misiti, M., Misiti, Y., et al.: Wavelet Toolbox User’s Guide. The Math Works Inc. (1996)
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Guo, Q., Yu, H., Xu, A. (2006). A New Intelligent Diagnostic Method for Machine Maintenance. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_79
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DOI: https://doi.org/10.1007/11739685_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
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