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

Content Provider	IEEE Xplore Digital Library
Author	Bilgili, E. Nucan, O. Muhittin Albora, A. Cem Goknar, I.
Copyright Year	2002
Description	Author affiliation: TUBITAK Marmara Research Center, Gebze High Technol. Inst., Turkey (Bilgili, E.)
Abstract	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 template optimisation using neighbourhood relationships of the pixels. The residual anomaly separation used in location decisions is one of the main problems in geophysics. The method proposed here is used in evaluating the Dumluca iron ore region of Turkey.
Sponsorship	Johann Wolfgang Goethe Univ. IEEE Circuits & Syst. (CAS) Soc. German Nat. Soc. Found.(Deutsche Forschungsgeinschaft) Office of Naval Res. Int. Field Office US Navy (ONRIFO) USAF Eur. Office of Aerospace Res. & Dev.(EOARD)
Starting Page	391
Ending Page	398
File Size	295150
Page Count	8
File Format	PDF
ISBN	981238121X
DOI	10.1109/CNNA.2002.1035075
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-07-24
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	World Scientific Publishing Co Pte Ltd.
Subject Keyword	Cellular neural networks Magnetic separation Image processing Geophysics Magnetic fields Filtering Genetic algorithms Stochastic processes Pixel Iron
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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