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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...
Motivated by the slow learning properties of multilayer perceptrons which utilize computationally intensive training algorithms and can get trapped in local minima, this work deals with ridge polynomial neural networks (RPNN) and least-square support vector machines (LSSVM) technique. RPNN and LSSVM are combined with the finite element method (FEM), to evaluate the dielectric materials properties...
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