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Improvement Efficiency of Radial Basis Function Based on the Optimization of its Parameters using Particle Swarm Optimization | ||
| Earth Observation and Geomatics Engineering | ||
| مقاله 10، دوره 6، شماره 2، اسفند 2022 اصل مقاله (1.42 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2023.351405.1126 | ||
| نویسندگان | ||
| Majid Malekpour Golsefidi؛ Rahim Ali Abbaspour* | ||
| University of Tehran | ||
| چکیده | ||
| One of the most widely used interpolation methods is the application of radial basis functions (RBF) to achieve a global and exact surface. Because of the computational complexity and fluctuation of the fitted surface achieved by the RBF method, the interpolation method is converted to a radial basis function neural network (RBFNN) model to solve these problems. Particle swarm optimization (PSO) algorithm is used in the designing process of a neural network to determine the optimal values of the center and radius of each basis function. In addition, the weights of radial basis functions are determined by calculating the pseudo-inverse matrix of coefficients. Finally, the accuracy of the proposed method was evaluated using the root mean square error (RMSE) in areas with different elevation ranges. Consequently, the proper type of the radial basis function was selected based on the RMSE in each area. As a result of the study, RBFNN model has a higher accuracy compared to other interpolation methods especially the RBF interpolation method. | ||
| کلیدواژه‌ها | ||
| Radial basis function؛ neural network؛ interpolation؛ particle swarm optimization؛ clustering | ||
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آمار تعداد مشاهده مقاله: 147 تعداد دریافت فایل اصل مقاله: 168 |
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