Abstract
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.
Highlights
Low profile, conformable to planar and nonplanar surfaces, most economical, mechanically robust, light weight, and mount-ability are the key advantages of microstrip antennas (MSAs)
The computed seven different parameters of rectangular microstrip antenna (RMSA), circular microstrip antenna (CMSA), and triangular microstrip antenna (TMSA) are given in Table 6, whereas their reference counterparts are mentioned in Tables 1, 2, and 3, respectively
A comparison between the present method results and previously computed neural networks results using software implementations [5,6,7,8,9,10,11] is given in Table 7, which shows that, in the neural models [5, 10,11,12], the total absolute error for resonance frequency of RMSAs is calculated as 751.0 MHz, 750.0 MHz, 203.6 MHz, and 557.1 MHz, whereas, in the present model, it is only 55.2 MHz
Summary
Conformable to planar and nonplanar surfaces, most economical, mechanically robust, light weight, and mount-ability are the key advantages of microstrip antennas (MSAs). To et al [28] have described prototyping of a neuroadaptive smart antenna beam-forming algorithm using hardware-software approach by implementing the RBF neural network on FPGA platform. Ghayoula et al [30] have explored prototyping and implementing concept of neural networks on FPGA platform for designing phased antenna array In this purpose, they have optimized the neural model with 17 neurons in the input layer and 8 neurons in the output layer. The beauty of the present work lies in creating an FPGA-based reconfigurable hardware for a generalized neural model which is capable of computing seven different performance parameters. In the proposed work, training of the model is done offline in personal computing machine and the testing algorithm is implemented on Xilinx’s FPGA board, XC3S500E
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