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المؤلفMamlook, Rustom
تاريخ الإتاحة2009-11-25T13:03:56Z
تاريخ النشر1996
اسم المنشورEngineering Journal of Qatar University
الاقتباسEngineering Journal of Qatar University, 1996, Vol. 9, Pages 133-146.
معرّف المصادر الموحدhttp://hdl.handle.net/10576/7859
الملخصA self-organizing multisensor fusion algorithm to classify the inputs (data or images) into classes (targets, backgrounds) is presented. The algorithm forms clusters and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of inputs. The algorithm is a self-organizing algorithm, since it has the ability to form and adjust the number of clusters without being given the correct number of clusters. This algorithm implements a clustering algorithm that is very similar to the simple sequential leader clustering algorithm and the Carpenter/Grossberg net algorithm (CGNA). The algorithm differs from CGNA in that (1) the data inputs and data pointers may take on real values, (2) it features an adaptive mechanism for selecting the number of clusters, and (3) it features an adaptive threshold. The algorithm does not require the number of classes been known apriori. The problem of threshold selection is considered and the convergence of the algorithm is shown. An example is given to show the application of the algorithm for multisensor fusion for classifying targets and backgrounds, and the results of using this algorithm is compared to the results of using K-nearest neighbor algorithm.
اللغةen
الناشرQatar University
الموضوعEngineering: Research & Technology
العنوانA Self-Organizing Multisensor Fusion Classification Algorithm
النوعArticle
الصفحات133-146
رقم المجلد9
dc.accessType Open Access


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