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A RBF/MLP Modular Neural Network for Microwave Device Modeling


M?rcio G. Passos, Paulo H. da F.Silva, Humberto C.C. Fernandes


Vol. 6  No. 5  pp. 81-86


This work presents a new Radial Basis Function/Multilayer Perceptron (RBF/MLP) modular structure, training with the efficient Resilient Backpropagation (Rprop) algorithm, that has been used for nonlinear device modeling in microwave band. The proposed modular configuration employs three or more nets, each one with a hidden layer of neurons. This method was proposed on the basis of the different characteristics of the two networks types: The MLP networks construct global approximations to nonlinear input-output mapping, consequently they are able to generalize in those regions of the input space where little or no training data is available. However, RBF networks use exponentially decaying localized nonlinearities to construct local approximations to nonlinear input-output mapping. Simulations through the proposed neural network models for microwave waveguide and patch antenna on PBG (Photonic Bandgap) structures and gave answers in excellent agreement with accurate results (measured or simulated) available in the literature.


Neural networks, Data modeling, Computational methods