Design and Analysis of Microstrip Antenna Using Multilayer Feed-Forward Back-Propagation Neural Network (MLPFFBP-ANN)

Singh, Prashant; Singh, Vinod Kumar; Lala, Archana; Bhoi, Akash Kumar

doi:10.1007/978-981-10-7901-6_43

Cited by 7 publications

(1 citation statement)

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“…Artificial neural networks, which are widely used in data prediction, use mathematical methods to simulate the structure, function, and mode of processing of biological neural systems to build a complete information processing system. In this paper, the BP neural network model [39,40], which is relatively mature and widely used, is used to fill the modified atmospheric refractive index of the middle layer (100°m-3000°m) by inputting the modified atmospheric refractive index at the lower (0-100°m) and higher (3000°m-10000°m) layers.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Using Improved XGBoost Algorithm to Obtain Modified Atmospheric Refractive Index

Mai

Sheng

Shi

et al. 2021

International Journal of Antennas and Propagation

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Atmospheric refraction is a special meteorological phenomenon mainly caused by gas molecules and aerosol particles in the atmosphere, which can change the propagation direction of electromagnetic waves in the atmospheric environment. Atmospheric refractive index, an index to measure atmospheric refraction, is an important parameter for electromagnetic wave. Given that it is difficult to obtain the atmospheric refractive index of 100 meters (m)–3000°m over the ocean, this paper proposes an improved extreme gradient boosting (XGBoost) algorithm based on comprehensive learning particle swarm optimization (CLPSO) operator to obtain them. Finally, the mean absolute percentage error (MAPE) and root mean-squared error (RMSE) are used as evaluation criteria to compare the prediction results of improved XGBoost algorithm with backpropagation (BP) neural network and traditional XGBoost algorithm. The results show that the MAPE and RMSE of the improved XGBoost algorithm are 39% less than those of BP neural network and 32% less than those of the traditional XGBoost. Besides, the improved XGBoost algorithm has the strongest learning and generalization capability to calculate missing values of atmospheric refractive index among the three algorithms. The results of this paper provide a new method to obtain atmospheric refractive index, which will be of great reference significance to further study the atmospheric refraction.

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Section: Bp Neural Networkmentioning

confidence: 99%