Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in the Recognition of Videotaped Neonatal Seizures

This paper introduces a learning algorithm that can be used for training reformulated radial basis function neural networks (RBFNNs) capable of identifying uncertainty in data classification. The proposed learning algorithm is used to train a special class of reformulated RBFNNs, known as cosine RBFNNs, to recognize neonatal seizures based on feature vectors obtained by quantifying motion in their video recordings. The experiments verify that cosine RBFNNs trained by the proposed learning algorithm are capable of identifying uncertainty in data classification, a property that is shared by quantum neural networks but not by cosine RBFNNs trained by the original learning algorithm and conventional feed-forward neural networks.