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 configuration 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 specific 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.