Comparaison entre réseau de neurones et LMS à référence filtrée appliqués au contrôle actif de bruit
Keywords:
Algorithms, Active noise control (ANC)Abstract
L’algorithme généralement utilisé pour des problèmes de contrôle actif de bruit est du type LMS. Celui-ci est bien connu, facile à implanter et converge rapidement vers la solution optimale. Cependant, le LMS est sensible aux perturbations externes. Les réseaux de neurones sont, pour leur part, bien adaptés aux systèmes non-linéaires. En effet, la sortie d’un neurone est modulée par une fonction sigmoïde, dans le cas présent, une tangente hyperbolique. Cela implique qu’une modélisation par un réseau de neurones intègre la saturation qui intervient sur le signal de commande à la sortie du processeur. La présente étude compare le comportement d’un algorithme FX-LMS (LMS normalisé à référence filtrée) à celui d’un réseau de neurones à référence filtrée face à une perturbation externe.
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