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Sensitivity of neural networks which approximate the Neyman-Pearson detector to threshold variations.

, , , и . EUSIPCO, стр. 1-4. IEEE, (2005)

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Jan Pilar

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Approximating the Neyman-Pearson Detector for Swerling I Targets with Low Complexity Neural Networks., , , , и . ICANN (2), том 3697 из Lecture Notes in Computer Science, стр. 917-922. Springer, (2005)Improving neural classifiers for ATR using a kernel method for generating synthetic training sets., , , и . NNSP, стр. 425-434. IEEE, (2002)Estimation of ocean wave heights from temporal sequences of X-Band marine radar images., , , и . EUSIPCO, стр. 1-5. IEEE, (2006)Doppler Processors as Suboptimum Approaches for Detecting Targets with Unknown Doppler Shift., , , , и . CICSyN, стр. 283-288. IEEE, (2013)Time-frequency analysis as a tool for improving neural detectors for low probability of false alarm., , , и . ICECS, стр. 91-94. IEEE, (2001)Enlarging Training Sets for Neural Networks., , , и . ANNs, стр. 25-33. INSTICC Press, (2004)On the Capability of Neural Networks to Approximate the Neyman-Pearson Detector: A Theoretical Study., , , и . ANNs, стр. 69-76. INSTICC Press, (2004)Using Multilayer Perceptrons to Align High Range Resolution Radar Signals., , , и . ICANN (2), том 3697 из Lecture Notes in Computer Science, стр. 911-916. Springer, (2005)SONN and MLP Based Solutions for Detecting Fluctuating Targets with Unknown Doppler Shift in Gaussian Interference., , , , и . IWANN (1), том 7902 из Lecture Notes in Computer Science, стр. 584-591. Springer, (2013)MLP and RBFN for Detecting White Gaussian Signals in White Gaussian Interference., , , и . IWANN (2), том 2687 из Lecture Notes in Computer Science, стр. 790-797. Springer, (2003)