Applications of Modular RBF/MLP Neural Networks in the Modeling of Microstrip Photonic Bandgap Structures

Abstract

This paper presents a Radial Basis Function/Multilayer Perceptron (RBF/MLP) modular neural network, training with the Resilient Backpropagation (Rprop) algorithm which has been used for nonlinear device modeling in microwave band. The proposed modular con- flguration employs three or more neural networks, each one with a hidden layer of neurons, and aim to take advantage of the MLP and RBF networks speciflc characteristics to improve learning aspects, such as: ability to learn, speed of training and learning with consistency, or generalization. Simulations through the proposed neural network models for microstrip line with anisotropic PBG (Photonic Bandgap) structure and a metallic enclosure microstrip with PBG gave responses in good agreement with accurate results (measured or simulated) available in the literature.