COMPUTATIONAL INVESTIGATION ON THE USE OF FEM AND MLP NEURAL NETWORK IN THE INVERSE PROBLEM OF DEFECTS IDENTIFICATIONS

Yıl 2008, Cilt: 8 Sayı: 1, 537 - 548, 02.01.2012

T. Hacıb M. R. Mekıdeche N. Ferkha

Öz

COMPUTATIONAL INVESTIGATION ON THE USE OF FEM AND MLP NEURAL NETWORK IN THE INVERSE PROBLEM OF DEFECTS IDENTIFICATIONS

Anahtar Kelimeler

Inverse problem, MLP neural network, defect identification, FEM

Kaynakça

• Ramuhalli. P., Neural network based iterative algorithms for solving electromagnetic NDE inverse problems, Ph.D. dissertation, Dept. Elect. Comp. Eng, Iowa Univ, USA, 2002.
• Coccorese. E., Martone. R., Morabito. F. C., "A neural network approach for the solution of electric and magnetic inverse problems", IEEE Trans. Magnetics, Vol: 30, No: 5, pp. 2829- , 1994.
• Hoole. S. R. H., "Artificial neural networks in the solution of inverse electromagnetic field problems", IEEE Trans. Magnetics, Vol: 29, No: , pp. 1931-1934, 1993.
• Ramuhalli. P., Udpa. L., Udpa. S. S., "Finite element neural networks for solving differential equations", IEEE Trans. Neural Networks, Vol: , No: 6, pp. 1381-1392, 2005.
• Wong. P. M., Nikravesh. M., "Field applications of intelligent computing techniques", J. Petrol Geolog, Vol: 24, No: 4, pp. 381-387, 2001.
• Fanni. A., Montisci. A., "A neural inverse problem approach for optimal design", IEEE Trans. Magnetics, Vol: 39, No: 3, pp. 1305- , 2003.
• Haykin. S., Neural networks: A comprehensive foundation, Englewood Cliffs, NJ: Prentice-Hall, New York, 1999.
• Turchenko. I. V., "Simulation modelling of multi-parameter sensor signal identification using neural networks", Proc. 2 IEEE Int Conf. Intelligent Systems, Bulgaria, 2004, pp. 48-53.
• De Alcantara. N.P., Alexandre. J., De Carvalho. M., "Computational investigation on the use of FEM and ANN in the non-destructive analysis of metallic tubes", Proc. 10 Conf. Electromagnetic Field Computation, Italy, Jain. A. K., Mao. J., Mohiuddin. K. M., Artificial neural networks: a tutorial, Computer, pp. 31-44, 1996.
• Mehrotra. K., Mohan. C. K., Ranka. S., Elements of artificial neural networks, MA: MIT Press, Cambridge, 1997.
• Cherubini. D., Fanni. A., Montisci. A., Testoni. P., "A fast algorithm for inversion of MLP networks in design problems", COMPEL. Int. J. Comp and Math in Electric and Electro Eng, Vol: 24, No: 3, pp. 906-920, 2005.
• Chari. M. V. K., Salon. S. J., Numerical methods in electromagnetism, CA: Academic, San Diego, 2000. nd IEEE Int Conf. th Biennial
• Silvester. P. P. and Ferrari. R. L., Finite elements for electrical engineers, Univ Press, Cambridge, 1996.
• Raida. Z., Modeling EM tructures in the neural network toolbox of MATLAB, IEEE Antenna’s and propagation Magazine, Vol: 44, No: 6, pp. 46-67, 2002.
• Partial Differential Equation Toolbox user’s guide, for use with MATLAB, The Math Works Inc. Han. W., Que. P., "2D defect reconstruction from MFL signals based on genetic optimization algorithm", Proc. 1 IEEE Int Conf. Industrial Technology, China, 2005, pp. 508-513.
• Chady. T., Enokizono. M., Sikora. R., Todaka. T., Tsuchida. Y., "Natural crack recognition using inverse neural model and multi-frequency eddy current method", IEEE Trans. Magnetics, Vol: 37, No: 4, pp. 2797- , 2001.
• Hagan. M. T., Menhaj. M., "Training feed-forward networks with the Levenberg- Marquardt algorithm", IEEE Trans Neural Networks, Vol: 5, No: 6, pp. 989-993, 1994.