Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Importance Sampling and Mean-Square Error in Neural Detector Training
Neural Processing Letters
Importance Sampling Techniques in Neural Detector Training
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Neyman-Pearson Neural Detectors
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
MLPs for detecting radar targets in gaussian clutter
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
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
IEEE Transactions on Signal Processing
MLP-based radar detectors for Swerling 1 targets
Pattern Recognition and Image Analysis
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 network detectors for composite hypothesis tests
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
NN-Based detector for known targets in coherent weibull clutter
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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Often, Neural Networks are involved in binary detectors of communication, radar or sonar systems. The design phase of a neural network detector usually requires the application of Monte Carlo trials in order to estimate some performance parameters. The classical Monte Carlo method is suitable to estimate high event probabilities (higher than 0.01), but not suitable to estimate very low event probabilities (say, 10^-5 or less). For estimations of very low false alarm probabilities (or error probabilities), a modified Monte Carlo technique, the so-called Importance Sampling (IS) technique, is considered in this paper; some topics are developed, such as optimal and suboptimal IS probability density functions (biasing density functions), control parameters and new algorithms for the minimization of the estimator error. The main novelty of this paper is the application of an efficient IS technique on neural networks, drastically reducing the number of patterns required for testing events of low probability. As a practical application, the IS technique is applied to a neural detector on a radar (or sonar) system.