Comparison between wavelet and radial basis function neural networks for GPS Prediction

Abstract EN

-Neural networks are complex nonlinear models; this characteristic enables them to be used in nonlinear system modeling and prediction applications.

The estimation and prediction are important roles in the communication system.

The proposed approach based on the Wavelet Neural Networks (WNNs) uses morlet as an activation function in the hidden layer of the wavelet neural network, while the Radial Basis Function Neural Networks (RBFNNs) use basis function that can be calculated as a Gaussian function.

In this paper, a comparison between the performance of Wavelet Neural Networks and Radial Basis Function for GPS prediction is presented.

The comparison results (using MATLAB programming) present that the Wavelet Neural Networks method has a great approximation ability, suitability and more stable in Global Positioning System (GPS) prediction than the Radial Basis Function Neural Networks, were highly effective predictions for accurate positioning and RMS errors are 0.05 meter after using of Wavelet Neural Networks predicti