Microwave characterization using ridge polynomial neural networks and least-square support vector machines
Tarik Hacib, Hulusi Açikgöz, Yann Le Bihan, O. Meyer, Lionel Pichon · 2010
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. RPNN maintain fast learning properties and powerful mapping capabilities of single layer high order neural networks. LSSVM is a statistical learning method that has good generalization capability and learning performance. Experimental results show that LSSVM can achieve good accuracy and faster speed than those using conventional methods.