Performance of Neural Networks in Classifying Environmentally Distorted Transient Signals

Autor:	R. L. Field, Patrick K. Simpson, E. J. Yoerger
Rok vydání:	1990
Předmět:	Neutral network Engineering Intelligent Network Artificial neural network business.industry Acoustics Electronic engineering Underwater Underwater acoustics business Frequency modulation Transient signal
DOI:	10.21236/ada230739
Popis:	Neutral networks have been showing great promise in several areas, one of which is the classification of underwater acoustic transients. The classification of low-frequency underwater acoustic transient signals using a neural network based system is investigated. The received acoustic transients are simulated using a time-domain parabolic equation model. The neural network is trained on three source signals and tested by classifying the same signals at 25 different receiver locations in a noise-free, range-dependent (upslope) environment. Overall classification performance is above 90%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e87c8e9bee8a347b16f44374a2f26c17 https://doi.org/10.21236/ada230739 Zobrazit plný text záznamu