Potential anomaly separation using genetically trained multi-level cellular neural networks

In this paper, multi-level genetic cellular neural networks (ML-GCNN) are applied to the geophysical problem of potential anomaly separation and satisfactory results are obtained, compared to classical deterministic approaches. ML-GCNN is a stochastic image processing technique which is based on tem...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Bilgili, E., Nucan, O., Muhittin Albora, A., Cem Goknar, I.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Cellular neural networks Filtering Genetic algorithms Geophysics Image processing Iron Magnetic fields Magnetic separation Pixel Stochastic processes
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!