Solution of inverse heat conduction problems using Kalman filter-enhanced Bayesian back propagation neural network data fusion

International Journal of Heat and Mass Transfer

Volumn 50, Issue 11-12, 2007, Pages 2089-2100

  Deng, S ^a   Hwang, Y ^a  

^a NATIONAL DEFENSE UNIVERSITY   (Taiwan)

Author keywords

Back propagation neural network (BPNN); Data fusion; Inverse heat conduction problem (IHCP); Kalman filtering (KF)

Indexed keywords

BACKPROPAGATION; COMPUTER SIMULATION; KALMAN FILTERS; NEURAL NETWORKS; PROBLEM SOLVING; SENSOR DATA FUSION;

BACK PROPAGATION NEURAL NETWORK (BPNN); INVERSE HEAT CONDUCTION PROBLEM (IHCP);

HEAT CONDUCTION;

BACKPROPAGATION; COMPUTER SIMULATION; HEAT CONDUCTION; KALMAN FILTERS; NEURAL NETWORKS; PROBLEM SOLVING; SENSOR DATA FUSION;

EID: 33947253481     PISSN: 00179310     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijheatmasstransfer.2006.11.019     Document Type: Article

References (31)

1. Beck J.V., Blackwell B., and St. Clair C.R. Inverse Heat Conduction Ill-posed Problems (1985), Wiley, New York pp. 1-8
2. Al-Khalidy N. Analysis of boundary inverse heat conduction problems using space marching with Savitzky-Gollay digital filter. Int. Commun. Heat Mass Transfer 26 2 (1999) 199-208
3. Shin M.S., and Lee J.W. Prediction of the inner wall shape of an eroded furnace by the nonlinear inverse heat conduction technique. JSME Int. J. B 43 4 (2000) 544-549
4. Maillet D., Degiovanni A., and Pasquetti R. Inverse heat conduction applied to the measurement of heat transfer coefficient on a cylinder. Comparison between an analytical and a boundary element technique. J. Heat Transfer, Trans. ASME 113 3 (1991) 549-557
5. Chen T.C., and Tuan P.C. Input estimation method including finite-element scheme for solving inverse heat conduction problems. Num. Heat Transfer, Part B: Fundamentals 47 3 (2005) 277-290
6. Dowding K.J., and Beck J.V. A sequential gradient method for the inverse heat conduction problem (IHCP). J. Heat Transfer, Trans. ASME 121 (1999) 300-306
7. Lesnic D., Elliott L., and Ingham D.B. Application of the boundary element method to inverse heat conduction problems. Int. J. Heat Mass Transfer 39 7 (1996) 1503-1517
8. Dumek V., Druckmuller M., Raudensky M., and Woodburg K.A. Novel approaches to the IHCP: neural networks and expert systems. Proc 1 Int. Conf. Inverse Probl. Eng. Theory Pract. (1993), ASME, New York 275-282
9. Raudensky M., Horsky J., and Krejsa J. Usage of neural network for coupled parameter and function specification inverse heat conduction problem. Int. Commun. Heat Mass Transfer 22 5 (1995) 661-670
10. Krejsa J., Woodbury K.A., Ratliff J.D., and Raudensky M. Assessment of strategies and potential for neural networks in the inverse heat conduction problem. Inverse Probl. Eng. 7 (1999) 197-213
11. Shiguemori Elcio H., Da Silva José DemÍSio S., and De Campos Velho Haroldo F. Estimation of initial condition in heat conduction by neural network. Inverse Probl. Sci. Eng. 12 3 (2004) 317-328
12. Klein L.A. Sensor and Data Fusion Concepts and Applications. second ed. (1999), SPIE-International Society for Optical Engine, Bellingham, WA pp. 47-81
13. Luo R.C., and Kay M.G. Multisensor integration and fusion in intelligent systems. IEEE Trans. Syst., Man Cybernet. 19 5 (1989) 901-931
14. Chin L. Application of neural networks in target tracking data fusion. IEEE Trans. Aerospace Electron. Syst. 30 1 (1994) 281-287
15. V. Vaidehi, N. Chitra, C.N. Krishnan, M. Chokkalingam, Neural network aided Kalman filtering for multitarget tracking applications, in: IEEE Proceedings of the Radar Conference, Waltham, MA, 1999, pp. 160-165.
16. Y.C. Wong and M.K. Sundareshan, Data fusion and tracking of complex target maneuvers with a simplex-trained neural network-based architecture, in: IEEE World Congress on Computational Intelligence, Anchorage, AK, vol. 2, 1998, pp. 1024-1029.
17. Aykan R., Hajiyev C., and Caliskan F. Kalman filter and neural network-based icing identification applied to A340 aircraft dynamics. Aircraft Eng. Aerospace Technol.: An Int. J. 77 1 (2005) 23-33
18. M. Riedmiller, H. Braun, A direct adaptive method for faster backpropagation learning: the RPROP algorithm, in: Proceedings of the IEEE International Conference on Neural Networks, San Francisco, 1993, pp. 586-591.
19. Hagan M.T., Howard B., Demuth M.H., and Beale M.H. Neural Network Design (2002), PWS Publishing Company, Boston (Chapter 9 (pp. 15-19))
20. Haykin S. Neural Networks: A Comprehensive Foundation (1998), Prentice Hall, New Jersey p. 239
21. Moller M.F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6 (1993) 525-533
22. Battiti R. First and second order methods for learning: between steepest descent and Newton's method. Neural Comput. 4 2 (1992) 141-166
23. Powell M.J.D. Restart procedures for the conjugate gradient method. Math. Program. 12 (1977) 241-254
24. Tuan P.C., Ji C.C., Fong L.W., and Huang W.T. An input estimation approach to on-line two dimensional inverse heat conduction problems. Num. Heat Transfer B 29 (1996) 345-363
25. Hopfield J.J. Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. USA 81 (1984) 3088-3092
26. Hopfield J.J., and Tank D.W. Computing with neural circuits: a model. Science 233 (1986) 625-633
27. Deng S., and Hwang Y. Applying neural networks to the solution of forward and inverse heat conduction problems. Int. J. Heat Mass Transfer 49 25-26 (2006) 4732-4750
28. MacKay D.J.C. Bayesian interpolation. Neural Comput. 4 (1992) 415-447
29. F.D. Foresee, M.T. Hagan, Gauss-Newton approximation to Bayesian learning, in: Proceedings of the 1997 International Joint Conference on Neural Networks, Houston, 1997, pp. 1930-1935.
30. Kreyszig E. Advanced Engineering Mathematics. seventh ed. (1993), Wiley, New York pp. 648-649
31. Carslaw H.S., and Jaeger J.C. Conduction of Heat in Solids. second ed. (1959), Oxford University Press, New York pp. 203, 328-329