In the last one decade, neural networks-based modeling has been used for computing different performance parameters of microstrip antennas because of learning and generalization features. Most of the created neural models are based on software simulation. As the neural networks show massive parallelism inherently, a parallel hardware needs to be created for creating faster computing machine by taking the advantages of the parallelism of the neural networks. This paper demonstrates a generalized neural networks model created on field programmable gate array- (FPGA-) based reconfigurable hardware platform for computing different performance parameters of microstrip antennas. Thus, the proposed approach provides a platform for developing low-cost neural network-based FPGA simulators for microwave applications. Also, the results obtained by this approach are in very good agreement with the measured results available in the literature. 1. Introduction Low profile, conformable to planar and nonplanar surfaces, most economical, mechanically robust, light weight, and easily mount-ability are the key advantages of microstrip antennas (MSAs). Because of these add-on advantages, the microstrip antennas are widely used in many communication applications. Since the microstrip antenna operates only in the vicinity of the resonant frequency, it needs to be calculated accurately for analyzing the microstrip antennas. Similarly, for designing the microstrip antennas, the physical dimension(s) must also be calculated precisely [1]. There are two conventional ways for analyzing and/or designing the microstrip antennas, analytical methods and numerical methods. The analytical methods provide a good spontaneous explanation for the operation of microstrip antennas. As the analytical methods are based on the physical assumptions for simplifying the radiation mechanism of the microstrip antennas, these methods are not suitable for many structures, where the thickness of the substrate is not very thin. The numerical methods also provide the accurate results but the analysis using these methods leads to the expressions as an integral equation. The choice of test functions and path integrations appears to be more critical without any initial assumption in the final stage of the numerical results. Also, these methods require a new solution for any sort of alteration in the geometry. The problems associated with these conventional methods can be overcome by selecting the appropriate neural network methods [1]. In recent years, artificial neural networks (ANNs) have acquired
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