Comparison of Neural Network Learning Algorithms for Prediction Enhancement of a Planning Tool
نویسندگان
چکیده
This work presents the results of the studies concerning the application of different neural network training algorithms to enhance the prediction of a radio network planning tool. Investigations are made on a hybrid model that combines the a-priori information in form of simulation results with the a-posteriori knowledge contained in measurement data. The performances of Back Propagation and Levenberg-Marquardt algorithms are compared to the measured values. The comparison is based on the absolute mean square error, standard deviation and root mean square error between predicted and measured values. The study is made on the Empirical Risk Minimization context and the neural network generalization error (Real Risk) is given with a 95% confidence interval. Key-Words– Neural Networks, Back-Propagation, Levenberg-Marquardt, Radio Network Planning ToolPrediction Enhancement
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