International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 6, No. 10, 8 Sep. 2014
Cover page and Table of Contents: PDF (size: 772KB)
A New Application of an ANFIS for the Shape Optimal Design of Electromagnetic Devices
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Index Terms
Adaptive Neuro-Fuzzy Inference System, Simulated Annealing, Genetic Algorithm, Shape Optimization, Electromagnetic Actuator
Abstract
This paper presents a new model based on simulated annealing algorithm (ASA) and adaptive neuro-fuzzy inference system (ANFIS) for shape optimization and its applications to electromagnetic devices. The proposed model uses ANFIS system to evaluate the electromagnetic performance of the device. Both the ANFIS and ASA method are applied to the design/optimization of the electromagnetic actuator. The results of the proposed approach are compared with other techniques such as: method of moving asymptotes, penalty method, augmented lagrangian genetic algorithm and simulated annealing method (SA). Among the algorithms, the proposed ANFIS-ASA approach significantly outperforms the other methods.
Cite This Paper
N. Mohdeb, T. Hacib, "A New Application of an ANFIS for the Shape Optimal Design of Electromagnetic Devices", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.10, pp.11-19, 2014. DOI:10.5815/ijisa.2014.10.02
Reference
[1]R. H. Bishop, The Mechatronics Handbook, CRC Press, London, UK, 2008.
[2]G. Drago, A. Manell, M. Nervi, M. Reppetto, G. Secondo, A combined Strategy for optimization in non-linear magnetic problems using simulated annealing and search techniques, IEEE Trans. Mag, vol. 35, N. 3, pp 1702-1705, 1999.
[3]G. Aiello, S. Alfonzetti, Stochastic Optimization of an Electromagnetic Actuator by Means of Dirichlet Boundary Condition Iteration, IEEE Trans. Mag, vol. 36, N. 4, pp 1110- 1114, 1999.
[4]R. R. Saldanha, Optimisation en électromagnétisme par application conjointe des méthode de programmation non linéaire et de la méthode des éléments finis, PHD Thesis, National Institute of Applied Sciences in Grenoble, French, 1997.
[5]J. M. Biedinger , Shape Sensitivity Analysis of Magnetic Forces, IEEE Trans. Mag, Vol. 33, N. 3, pp 1110- 1114, 1997.
[6]J. R. Jang, “ANFIS: Adaptive-Network-based Fuzzy Inference Systems,” IEEE Trans. Sys. Man. Cyb, vol. 23, No. 3, pp. 665-685, May 1993.
[7]W. Pedrycz, A. Kandel, “Neurofuzzy Systems,” In Fuzzy Systems: Modeling and Control, Kluwer Academic Publishers, 1999.
[8]P. P. Silvester, R. L. Ferrari, Finite Elements for Electrical Engineers, Cambridge University Press, Cambridge, UK, 1996.
[9]A. Benhama, A. C. Williamson, A. B. J. Reece, Virtual work approach to the computation of magnetic force distribution from finite element field solution, IEE Proc. elect. pow, Vol. 147, pp. 437-442, Mar. 2000.
[10]J.S. R. Jang, and C. T. Sun, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1997.
[11]J. Paul, T. Yusiong, “Optimizing Artificial Neural Networks using Cat Swarm Optimization algorithm”, International Journal Intelligent Systems and applications, Vol.1, pp. 69-80, 2013.
[12]G. Drago, A. Manell, M. Nervi, M. Reppetto, G. Secondo, A combined Strategy for optimization in non-linear magnetic problems using simulated annealing and search techniques, IEEE Trans. Mag, Vol. 35, N. 3, pp 1702-1705, 1999.
[13]S.Yang, J.-M. Machado,G.Ni, S.-L.Ho, and P.Zhou,“Aself-learning simulated annealing algorithm for global optimizations of electromagnetic devices,” IEEE Trans. Magn., vol.36, pp.1004–1008,July 2000.
[14]L. Ingber, ASA-User Manual, Available: www.ingber.com, 2003.
[15]L. Ingber, Simulated annealing: Practice versus theory, Math. Comput. Modelling, vol. 18, No. 11, pp. 29-57, Apr. 1993.
[16]N. Mohdeb, M. R. Mekideche, A fast hybrid algorithm for solving materials properties determination inverse problem, I. J. Comput. Science, vol.37, Issue 2, pp 18-26, Mai 2010.