Abstract—The rectangular ridged waveguide filter optimization is discussed. To improve the performances of the optimized filters, we exploit the height of the gaps between the ridges and the wave guide wall. We propose a hybrid technique, constituted by the artificial neural networks (ANNs) and the circuit theory. The proposed approach is applied to the design off our-pole and six-pole narrow band-pass filters. The results agree well with those produced by conventional simulators.
Index Terms—Rectangular ridged waveguides, filter optimization, multimodal variational method, artificial neuralnetworks.
M. Yahia is with LAPLACE, ENSEEIHT, MACS and ENIG(e-mail :email@example.com).
J.W. Tao is with Laboratoire Plasma et Conversion d’Energie(LAPLACE), ENSEEIHT, 2 rue Camichel 31071 Toulouse cedex Francetao@laplace.univ-tls.fr).
H. Benzina is with the national engineering school of Gabès (ENIG), RueOmar Ibn Elkhattab 6029 Gabès Tunisia (e-mail: firstname.lastname@example.org)
Mohamed N. Abdelkrim is with Unité de recherche Modélisation, Analyse et Commande de système (MACS), ENIG, Rue Omar Ibn Elkhattab6029 Gabès Tunisia (e-mail : email@example.com).
Cite: Mohamed Yahia, Jun W. Tao, Hafedh Benzina and Mohamed N. Abdelkrim, " Ridged Waveguide Filter Optimization Using the Neural Networks and a Modified Simplex Method," International Journal of Innovation, Management and Technology vol. 1, no. 3, pp. 259-263, 2010.