İşletme Bölümü / Department of Business Administration
Permanent URI for this collectionhttps://hdl.handle.net/11413/6793
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Browsing İşletme Bölümü / Department of Business Administration by Publisher "ELSEVIER SCIENCE INC, 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA"
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Publication B-spline solution of singular boundary value problems(ELSEVIER SCIENCE INC, 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA, 2006-11-15) Çağlar, Nazan; Çağlar, Hikmet; TR110809; TR114368Homogeneous and non-homogenous singular boundary value problems (special case) are solved using B-splines. The original differential equation is modified at singular point then the boundary value problem is treated by using B-spline approximation. The method is tested on some model problems from the literature, and the numerical results are compared with exact solution. (c) 2006 Elsevier Inc. All rights reserved.Publication The Modified Fuzzy Art and a Two-Stage Clustering Approach to Cell Design(ELSEVIER SCIENCE INC, 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA, 2007-12-01) Özdemir, Rifat Gürcan; Gençyılmaz, Güneş; AKTİN, AYŞE TÜLİN; TR141173; TR30141; TR109203This study presents a new pattern recognition neural network for clustering problems, and illustrates its use for machine cell design in group technology. The proposed algorithm involves modifications of the learning procedure and resonance test of the Fuzzy ART neural network. These modifications enable the neural network to process integer values rather than binary valued inputs or the values in the interval [0, 1], and improve the clustering performance of the neural network. A two-stage clustering approach is also developed in order to obtain an informative and intelligent decision for the problem of designing a machine cell. At the first stage, we identify the part families with very similar parts (i.e., high similarity exists in their processing requirements), and the resultant part families are input to the second stage, which forms the groups of machines. Experimental studies show that the proposed approach leads to better results in comparison with those produced by the Fuzzy ART and other similar neural network classifiers. (C) 2007 Elsevier Inc. All rights reserved.