Noise canceling with autoassociative memory trained by order statistics

Noise canceling with autoassociative memory trained by order statistics

In this paper, noise canceling using an autoassociative memory is considered for possible applications to constant signal detection. The authors use order statistics to help the neural network learn the noise characteristics. In essence, the performance of this neural network is shown to not depend...

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Hauptverfasser: Jinsoo Bae, Young Kwon Ryu, Iickho Song
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Filters
Gaussian noise
Neural networks
Noise cancellation
Noise figure
Noise reduction
Probability density function
Signal detection
Statistical distributions
Statistics
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Beschreibung
Zusammenfassung:In this paper, noise canceling using an autoassociative memory is considered for possible applications to constant signal detection. The authors use order statistics to help the neural network learn the noise characteristics. In essence, the performance of this neural network is shown to not depend on the distribution of noise, based on simulations for six well known noise probability density functions.< >
DOI:10.1109/ICNN.1994.374788