Microwave Characterization Using Ridge Polynomial Neural Networks and Least-Square Support Vector Machines

Abstract : This paper shows that Ridge Polynomial Neural Networks (RPNN) and Least-Square Support Vector Machines (LS-SVM) technique provide efficient tools for microwave characterization of dielectric materials. Such methods avoids the slow learning properties of multilayer perceptrons (MLP) which utilize computationally intensive training algorithms and can get trapped in local minima. RPNN and LS-SVM are combined in this work with the Finite Element Method (FEM) to evaluate the dielectric materials properties. The RPNN is constructed from a number of increasing orders of Pi-Sigma units, it maintains fast learning properties and powerful mapping capabilities of single layer High Order Neural Networks (HONN). LS-SVM is a statistical learning method that has good generalization capability and learning performance. The FEM is used to create the data set required to train the RPNN and LS-SVM. The performance of a LS-SVM model depends on a careful setting of its associated hyper-parameters. In this study the LS-SVM hyper-parameters are optimized by using a Bayesian regularization technique. Results show that LS-SVM can achieve good accuracy and faster speed than neural network methods.
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Contributor : Olivier Schneegans <>
Submitted on : Monday, June 25, 2012 - 12:08:47 PM
Last modification on : Thursday, November 22, 2018 - 2:55:23 PM

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Tarik Hacib, Yann Le Bihan, Mostafa-Kamel Smaïl, Mohamed Rachid Mekideche, Olivier Meyer, et al.. Microwave Characterization Using Ridge Polynomial Neural Networks and Least-Square Support Vector Machines. IEEE Transactions on Magnetics, Institute of Electrical and Electronics Engineers, 2011, 47 (5), pp.990-993. ⟨10.1109/TMAG.2010.2087743⟩. ⟨hal-00711555⟩

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