Abstract
A neurofuzzy modelling technique is used to predict the differential equation coefficients of brake noise time histories as functions of braking test conditions. These are then related to the 3rd order differential equations governing a candidate mathematical model of brake squeal using a second neurofuzzy model. This determines whether similar or sensible parametric changes in the model are required to mirror the dynamic effects of changes in experimental condition parameters. An assessment of the efficacy of the candidate model is then made based on this analysis. The results of different candidate models could be likewise compared to determine which is most realistic.
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References
Brown M. and Harris C.J (1994) Neurofuzzy adaptive modelling and control. Prentice Hall, Hemel Hempstead, UK 1994
Cox M. (1972) The numerical evaluation of B-splines. Jnl. Inst. Math. Appl. Vol 10, pp 134–149. 1972
DeBoor C (1972) On calculating with B-Splines J.Approx. Theory. Vol 6. Pp 50–62, 1972
Felske A, Hoppe G and Matthai, H (1978) Oscillations in squealing disc brakes — analysis of vibration modes by holographic interferometry. SAE Paper 780333
Kavli T (1992) ASMOD — an algorithm for Adaptive Spline Modelling of Observation Data. Int. Jnl. Of Control, vol 58, pp 947–967, 1992
Liles G (1989) Analysis of disc brake squeal using finite element methods. SAE paper No. 891150, 198
Rissanen J (1978) Modelling by shortest data description. Automatica, Vol. 14, pp 465–471, 1978
Shin K, Feraday S, Harris C.J. Brennan M (1999) Optimal auto-regressive modelling of a measured noisy time series using SVD. Proc. Internoise’99. Conference, Florida, USA. Dec 1999
Zadeh L (1965) Fuzzy Sets Jnl. Information and Control, vol 8, pp 338–353, 1965
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© 2000 Springer-Verlag Berlin Heidelberg
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Feraday, S., Harris, C., Shin, K., Brennan, M., Lindsay, M. (2000). The Use of AI Methods for Evaluating Condition Dependent Dynamic Models of Vehicle Brake Squeal. In: Logananthara, R., Palm, G., Ali, M. (eds) Intelligent Problem Solving. Methodologies and Approaches. IEA/AIE 2000. Lecture Notes in Computer Science(), vol 1821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45049-1_6
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DOI: https://doi.org/10.1007/3-540-45049-1_6
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