Approximating the Neyman-Pearson detector for swerling I targets with low complexity neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3697 LNCS, Issue , 2005, Pages 917-922

  De La Mata Moya, D ^a   Jarabo Amores, P ^a   Rosa Zurera, M ^a   Lopez Ferreras F ^a   Vicen Bueno, R ^a  

^a UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; BOUNDARY CONDITIONS; DECISION THEORY; GAUSSIAN NOISE (ELECTRONIC);

LM ALGORITHMS; NEYMAN-PEARSON DETECTOR; OPTIMUM DECISION BOUNDARIES;

NEURAL NETWORKS;

EID: 33646246402     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

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