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

Year 2008, Volume: 8 Issue: 1, 537 - 548, 02.01.2012

T. Hacıb M. R. Mekıdeche N. Ferkha

https://izlik.org/JA95TA53FN

Abstract

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

Keywords

Inverse problem , MLP neural network , defect identification , FEM

References

• 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.