Design and Optimization of Microstrip Patch Antennas using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract

Over the past one decade, there is a rapid growth in the development of various applications involving wireless communication. The performance of all such wireless systems depends on the design and proper functioning of the antenna. Microstrip antennas are preferred for majority of these applications. This is because of their inherent advantages, such as small size, planner structure and ease of fabrication. However, for all modern wireless applications, the design of microstrip antenna has become challenging, as several performance parameters, such as return-loss, gain, cross-polarization, side-lobes, etc. are to be optimized simultaneously. Conventionally, for design and analysis of microstrip antennas, methods such as, Finite Element Method (FEM), Full-wave Method of Moment (MoM), Finite Dif- ference Time Domain (FDTD), etc. are in use. However, these techniques suf- fer from a serious drawback of high computation time and high computational re- sources. As alternative to these conventional methods, recently, the use of soft computing techniques for design and analysis of Microstrip antennas has increased. The most recognized soft computing techniques are : (i)Arti cial Neural Networks (ANNs), (ii)Genetic Algorithm (GA), (iii)Fuzzy Logic Models (FLM), (iv)Partial Swarm Techniques (PST), (v) Adaptive Neuro-fuzzy Inference System (ANFIS). Out of all these techniques, ANFIS are most preferred technique for design and op- timization of microstrip patch antennas. It is an integration of both neural networks and fuzzy logic. In the present dissertation report, the analysis and synthesis of rectangular, cir- cular, equilateral triangular and elliptical microstrip patch antennas using ANFIS is presented. ANFIS based various CAD models have been developed are presented in the report. The gen s1 and gen s2 functions have been used to form network architectures of ANFIS based CAD models. The analysis model consists of antenna geometrical parameters as inputs and resonant frequency and return loss as outputs. In order to train these ANFIS models, the training data are obtained through mi- crostrip antenna full-wave solver. For all CAD models, the results of testing data are compared with the theoretical / simulated results and are thoroughly summarized in the report.