فهرست نشریات علمی دسترسی آزاد ایران

One of the most widely used interpolation methods is the application of radial basisfunctions (RBF) to achieve a global and exact surface. Because of the computationalcomplexity and fluctuation of the fitted surface achieved by the RBF method, theinterpolation method is converted to a radial basis function neural network (RBFNN)model to solve these problems. Particle swarm optimization (PSO) algorithm is used inthe 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 aredetermined by calculating the pseudo-inverse matrix of coefficients. Finally, theaccuracy 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 radialbasis 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 methodsespecially the RBF interpolation method.