Performance Analysis of Neural Network Detectors by Importance Sampling Techniques
Neural Processing Letters
Comparison of a neural network detector vs Neyman-Pearson optimal detector
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
MLP and RBFN for detecting white gaussian signals in white gaussian interference
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Approximating the Neyman-Pearson detector for swerling I targets with low complexity neural networks
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Neural networks for signal detection in non-Gaussian noise
IEEE Transactions on Signal Processing
Hi-index | 0.00 |
Evolutionary optimization algorithms contain, due to their heuristic inspiration, many heuristic parameters, which need to be empirically tuned for the algorithm to work most properly. This paper deals with tuning those parameters in situations when ...