Parameter Optimization in Design of a Microstrip Patch Antenna Using Adaptive Neuro-Fuzzy Inference System Technique

Abstract

The demand for small and reliable, high performance, diverse polarization, low-profile, and lightweight antennas has greatly increased. Its demand is in mobile communications, satellite communication, electronic warfare, biological telemetry, navigation, radar, and surveillance. Microstrip patch antennas are examples of low profile antennas. In the current highly demanding consumer world for microstrip patch antenna enabled systems, an effective and efficient higher manufacturing processing capability is required. There is thus the need for a fast, reliable, and effective microstrip patch antenna design procedure. In this thesis, an artificial intelligence technique is used to optimize the parameters used in the design of rectangular microstrip patch antennas. This is achieved by using Adaptive Neuro-Fuzzy Inference Technique (ANFIS) implemented on the MATLAB® platform. This optimization method is simple, effective, and has low computer memory usage. Various data sets were used in performing the optimization for various antenna parameters and the optimized simulated results obtained were used in fabricating a set of rectangular microstrip patch antennas. Simulation results obtained from commercial Antenna Magus software were used to validate the proposed design method and to fabricate a second set of patch antennas. Fabricated antennas were then experimentally tested.