DocumentCode :
296039
Title :
Performance improvements for a neural network detector
Author :
Andina, Diego ; Sanz-GonzÁlez, José L. ; Rodríguez-Martin, Octavio A.
Author_Institution :
ETSI Telecomunicacion, Univ. Politecnica de Madrid, Spain
Volume :
1
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
492
Abstract :
In this paper, a neural detector is purposed. It can be applied to binary detection problems such as those found in radar or sonar. Topics about designing the structure, training procedure and evaluating the performance, are discussed. The detector optimization is based on the use of a criterion function that yields a solution significantly superior to the typical sum-of-square-error. Using a modeled input, its performance is evaluated by Monte Carlo trials. As a result, receiver operating characteristics and detection curves are presented
Keywords :
Monte Carlo methods; acoustic signal detection; backpropagation; multilayer perceptrons; probability; radar detection; Monte Carlo trials; binary detection problems; detection curves; neural network detector; performance evaluation; radar; receiver operating characteristics; sonar; sum-of-square-error; training procedure; Detectors; Envelope detectors; Least squares approximation; Monte Carlo methods; Neural networks; Optical noise; Radar detection; Robustness; Sonar applications; Sonar detection; Telecommunication standards; Very large scale integration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.488226
Filename :
488226
Link To Document :
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