Warianty tytułu
Języki publikacji
Abstrakty
The problem of empirical data modeling is pertinent to several mechanics domains. Empirical data modeling involves a process of induction to build up a model of the system from which responses of the system can be deduced for unobserved data. Machine learning tools can model underlying non-linear function given training data without imposing prior restriction on the type of function. In this paper, we show how Support Vector Machines (SVM) can be employed to solve design problems involving optimizations over parametric space and parameter prediction problems that are recurrent in engineering domain. The problem considered is diffuser design where the optimal value of pressure recovery parameter can be obtained very efficiently by SVM based algorithm even in a large search space. In addition, locating the position of points on a string vibrating in a damped medium serves as an appropriate prediction problem. A grid-searching algorithm is proposed for automatically choosing the best parameters of SVM, thus resulting in a generic framework. The results obtained by SVM are shown to be theoretically sound and a comparison with other approaches such as spline interpolation and Neural Networks shows the superiority of our framework.
Rocznik
Tom
Strony
509-523
Opis fizyczny
Bibliogr. 26 poz., rys., tab., wykr.
Twórcy
autor
autor
autor
autor
- Indian Institute of Technology, Dept. of Applied Mechanics, New Delhi 110016
Bibliografia
- [1] R.S. Azad. Turbulent flow in a conical diffuser: a review. Experimental Thermal and Fluid Science, 13: 318-337, 1996.
- [2] D.R. Bland. Wave Theory and Applications, ch. 2, pp. 61-70. Clarendon Press, Oxford, 1988.
- [3] V. Cherkassky, Y. Ma. Practical selection of SVM parameters and noise estimation for SVM regression. Int. Journal of Neural Networks, 0893-6080, 17(1): 113-126, 2004.
- [4] N.M. Cho, C.A.J. Fletcher. Computation of turbulent conical diffuser flows using a non-orthogonal grid system. Computers and Fluids, 19: 347-361, 1991.
- [5] A. Davies, P. Samuels. An Introduction to Computational Geometry for Curves and Surfaces. Clarendon Press, 1996.
- [6] B.L. Fowler, E.M. Flint, S.E. Olson. Design methodology for particle damping. The SPIE Conference on Smart Structures and Materials, Newport Beach, CA, March 4-8, pp. 4331-20, 2001.
- [7] R.W. Fox, A.T. Mcdonald. Introduction to Fluid Mechanics, Ed. 4, ch. 8, pp. 352-354. John Wiley and Sons, 1995.
- [8] R.M. Glaese, E.H. Anderson. Vibration control using passive and active resonant devices. SPIE Smart Structures Conference, 2005.
- [9] P. Hajela, L. Berke. Neurobiological computational models in structural analysis and design. AIAA-90-1133-CP, 31st SDM Conf, Baltimore, MD, April 8-10, pp. 335-343, 1991.
- [10] F.M. Ham, I. Kostanic. Principles of Neurocomputing for Science and Engineering. McGraw-Hill, 2001.
- [11] S. Haykin. Neural Networks: A Comprehensive Foundation. Prentice Hall, 1998.
- [12] D.-S. Jeng, D.H. Cha, M. Blumenstein. Application of Neural Network in Civil Engineering Problems, The International Internet-Processing-Systems-Interdisciplinaries (IPSI-2003) Conference, Seveti-Stefan, Montenegro, October 5-11, 2003.
- [13] V. Kecman. Learning and soft computing Support Vector Machines, neural networks and fuzzy logic models. The MIT press, Cambridge, MA, 2001.
- [14] S.S. Keerthi, C.J. Lin. Asymptotic behaviors of Support Vector Machines with Gaussian kernel. Neural Computation, 1667-1689, 2003.
- [15] C. Kroll, J. Stedinger. Estimation of moments and quantiles using censored data. Water Resources Research, 32(4): 1005-1012, 1996.
- [16] H. Lee, L. Hajela. Prediction of turbine performance using MFN by reducing mapping nonlinearity. In: G.H.V. Topping, M. Papadrakakis, eds., Artificial Intelligence and Object Oriented Approaches for Structural Engineering, pp. 99-105, 1995.
- [17] T. Liszka. An interpolation method for an irregular net of nodes. Int. J. Num. Meth. Engrg., 20: 1599-1612, 1984.
- [18] A.T. McDonald, R.W. Fox. An experimental investigation of incompressible flow in conical diffusers. Int. J.Mech. Sci., 8: 125-139, 1966.
- [19] S. Mukherjee, E. Osuna, F. Girosi. Nonlinear prediction of chaotic time series using a support vector machine. In: J. Principle, L. Gile, N. Morgan, E. Wilson, eds., Neural Networks for Signal Processing VII — Proceedings of the 1997 IEEE Workshop, New York, 1997.
- [20] S.J. Schreck, W.E. Faller, M.W. Luttges. Neural network prediction of three-dimensional unsteady separated flow fields. J. Aircraft, 32: 178-185, 1995.
- [21] J.A. Suykens, J. Vandewalle, B.D. Moor. Optimal control by least squares support vector machines. Int. J. Neural Networks, 14(1): 23-35, 2001.
- [22] A. Tzes, J. Borowiec. Applications of fuzzy logic and neural networks to identification and control problems in fluid mechanics. AS ME International Mech. Engineering Congress and Exposition, Atlanta, Georgia, 1996.
- [23] R.D. Vanluchene, R. Sun. Neural networks in structural engineering. Microcomputers in Civil Engineering, 5(3): 207-215, 1990.
- [24] V. Vapnik, S. Golowich, A. Smola. Support vector method for function approximation, regression estimation, and signal processing. In: M. Mozer, M. Jordan, T. Petsche, eds., Advances in Neural Information Processing Systems 9, pp. 281-287. MIT Press, Cambridge, MA, 1997.
- [25] V. Vapnik. Statistical Learning Theory. Springer, NY, 1998.
- [26] W. Wysocki, Z. Kazimierski. Analysis of subsonic transitory stalled flows in straight-walled diffusers. ASME J. Fluids Engineering, 108: 222-226, 1986.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BPB1-0032-0038