PhD Thesis Defense Session

Abstract:
In the final step of any filter design process, the desired center frequency, coupling factor and external quality factor (Q_ext) are used to determine the physical parameters of the filter. Although in the most cases the physical dimensions of a single resonator for a given center frequency are determined using exact analytical or simple approximate equations, usually such simple equations cannot be found to easily relate the required coupling factor and Q_ext to the physical parameters of the filter. Analytical calculation of coupling factor and Q_ext versus dimensions are usually complicated due to the geometrical complexities or in some cases such as microstrip resonators due to the lack of exact solution for the field distribution. Therefore coupling factor and Q_ext of various kinds of resonators, especially microstrip resonators, are related to the physical parameters of the structure by the use of time consuming full wave simulations. In this dissertation a surprisingly fast and completely general approach based on a soft computing pattern-based processing technique, called active learning method (ALM) and spatial membership functions is proposed to overcome the time consuming process of coupling factor and Q_ext determination. ALM is an adaptive recursive fuzzy learning algorithm based on brain functionality and specifications which models a complex multi-input single-output (MISO) function or system as a fuzzy combination of some single-input single-output (SISO) one. In the modeling process the multi-dimensional functions of coupling factor and Q_ext are broken down into their simpler aspects, their behaviors are extracted and then final model will be constructed by combining these simpler aspects.

Date: 2012/3/6 Monday
Location: Room 302 Electrical Engineering Faculty