论文信息 - Application of radial basis function based neural networks to arrays of nonlinear antennas

Application of radial basis function based neural networks to arrays of nonlinear antennas

This paper presents numerical examples that shows the results predicted by the RBF-NN models are consistent with those calculated from existing studies. It should be noted that the RBF-NN model is inherently one type of the general regression. This makes it powerful in modeling and predicting such a nonlinear problem. With the use of neural networks, the complex numerical computation about arrays of nonlinear antennas can be replaced by a very simple algebraic operation as the neural networks are well trained. Although the training work of a RBF-NN model is usually time consuming, it can be completed in advance. This study is useful in the applications of antenna design, remote sensing and wireless communication

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