Neural-Memory Based Control of Micro Air Vehicles (MAVs) with Flapping Wings
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
MUSP'09 Proceedings of the 9th WSEAS international conference on Multimedia systems & signal processing
Designing neural networks for tackling hard classification problems
WSEAS TRANSACTIONS on SYSTEMS
A Note on a priori Estimations of Classification Circuit Complexity
Fundamenta Informaticae - Hardest Boolean Functions and O.B. Lupanov
RBF neural network based on q-Gaussian function in function approximation
Frontiers of Computer Science in China
| Hi-index | 0.00 |
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. This learning algorithm trains a special class of reformulated RBFNNs, known as cosine RBFNNs, by updating selected adjustable parameters to minimize the class-conditional variances at the outputs of their radial basis functions (RBFs). The experiments verify that quantum neural networks (QNNs) and cosine RBFNNs trained by the proposed learning algorithm are capable of identifying uncertainty in data classification, a property that is not shared by cosine RBFNNs trained by the original learning algorithm and conventional feed-forward neural networks (FFNNs). Finally, this study leads to a simple classification strategy that can be used to improve the classification accuracy of QNNs and cosine RBFNNs by rejecting ambiguous feature vectors based on their responses