Radial Basis Function Neural Network Model Training Efficiency and Generality Through Early Stopping Technique to Avert Over-Fitting for Robust Field Signal Strength Prediction

In this work, the right training criterion for optimal prediction of the desired output by the neural network model was our focus. The application of the early stopping technique in the neural network training of radial basis function model in comparison to the same model neural network training without application of early stopping technique was the basis for the work and the work validates the importance of early stopping technique for good network generalization through prevention of overfitting. An over-fitted radial basis function neural network model possesses the tendency for dataset memorization including noise and thus, poorly learns the dataset pattern. From the training results, the application of an early stopping technique inclusive of other vital hyper-parameters during the training of the radial basis function model demonstrates a measure that is very imperative in the prevention of over-fitting during network training. The training results show that the radial basis function model training without the application of an early stopping technique displayed a “learning and slow” phenomenon where the model stopped learning the dataset at a stage without optimum prediction of the dataset pattern. This phenomenon was evaded by the application of an early stopping technique at the ratio of 70%:15%:15% for training, testing, and validation of the standardized dataset. The radial basis function model neural network training regression results show optimum prediction results of R=0.99420 with a training set of R=0.9985 and a test set of R=0.9899 showing very close prediction of the training set by the test set on training with early stopping technique in comparison to the model training without the application of early stopping technique where R= 0.97200 with a training set of R= 0.9840 and a test set of R=0.9612 showing very wide prediction of the training set by the test set. Overfitting often results in good model training but poor test error as the network model learns the wrong structure in the training data or stops learning at a point as seen from the work. This was optimally evaded on the application of the early stopping technique in the work for robust prediction of signal power loss using a long-term evolution dataset.