RBF Neural Network based Model as an Optimal Classifier for the Classification of Radar Returns from the Ionosphere

Research into the problem of classification of radar returns from the ionosphere has been taken up as a challenging task for the neural networks (NNs). It appears from the literature review that for the Multi layer Perceptron (MLP) NN trained with backpropagation, reported average classification accuracy was about 96% on the test instances. This paper investigates and designs an optimal classifier using a radial basis function (RBF) NN. Authors compare the performance of two NN configurations, namely a well-known MLP NN model and the proposed RBF NN model on the radar dataset collected from the published studies. It is shown that the proposed RBF NN, consistently, has 100% accuracy on "bad" instances and 99.1935% accuracy on "good" instances. The results show that the proposed RBF NN classifier clearly outperforms the MLP NN one in various performance measures such as MSE, NMSE, correlation coefficients, area under the ROC curve and classification accuracy on the testing datasets even after attempting different data partitions.