Teknik Bilimler Meslek Yüksekokulu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/2671

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    Article
    Citation Count: 1
    Hücresel sinir ağları için gerilim kaynaklı hücre modelleri
    (AVES YAYINCILIK, 2001) Tander, Baran; Ün, Mahmut
    Bu makalede, bağımsız ve bağımlı gerilim kaynağı tabanlı yeni bir Hücresel Sinir Ağı hücre devresi önerilmiştir. Bu modelde akım kaynaklı Chua ve Yang ‘ın klasik hücre devresinin aksine hücreler için denge noktaları dinamik birimdeki Rx ve Cx’ den bağımsızdırlar. Tam bir hücre devresi tasarlanıp kararlı ve kararsız durumlar için benzetimleri yapılmıstır. Önerilen modelin avantaj ve dezavantajları sonuçlar bölümünde tartışılmıştır.
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    Conference Object
    Citation Count: 0
    Support vector machines based target tracking techniques
    (IEEE, 2006) Özer, Sedat; Çırpan, Hakan Ali; Kabaoğlu, Nihat
    This paper addresses the problem of aplying powerful statistical pattern classification algorithms based on kernels to target tracking. Rather than directly adapting a recognizer we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers using dynamic model together as feature vectors and makes the hyperplane and the support vectors follow the changes in these features. The performance of the tracker is demostrated in a sensor network scenario with a moving target in a polynomial route.
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    Conference Object
    Citation Count: 1
    Support vector machines based target tracking techniques [Destek vektör makineleri tabanlı hedef takip yöntemleri]
    (2006) Özer, Sedat; Cirpan, Hakan Ali; Kabaoğlu, Nihat
    This paper addresses the problem of aplying powerful statistical pattern classification algorithms based on kernels to target tracking. Rather than directly adapting a recognizer we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers using dynamic model together as feature vectors and makes the hyperplane and the support vectors follow the changes in these features. The performance of the tracker is demostrated in a sensor network scenario with a moving target in a polynomial route. © 2006 IEEE.