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Bölüm: Işık University, Faculty of Engineering, Department of Computer Engineering × Konu: Functions ×

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  • Küçük Resim
    Yayın
    A factorized high dimensional model representation on the nodes of a finite hyperprismatic regular grid
    (Elsevier Science inc, 2005-05-25) Tunga, Mehmet Alper; Demiralp, Metin
    When the values of a multivariate function f(x(1),...,x(N)), having N independent variables like x(1),...,x(N) are given at the nodes of a cartesian, product set in the space of the independent variables and ail interpolation problem is defined to find out the analytical structure of this function some difficulties arise in the standard methods due to the multidimensionality of the problem. Here, the main purpose is to partition this multivariate data into low-variate data and to obtain the analytical structure of the multivariate function by using this partitioned data. High dimensional model representation (HDMR) is used for these types of problems. However, if HDMR requires all components, which means 2(N) number of components, to get a desired accuracy then factorized high dimensional model representation (FHDMR) can be used. This method uses the components of HDMR. This representation is needed when the sought multivariate function has a multiplicative nature. In this work we introduce how to utilize FHDMR for these problems and present illustrative examples.
  • Küçük Resim
    Yayın
    Hybrid high dimensional model representation (HHDMR) on the partitioned data
    (Elsevier B.V., 2006-01-01) Tunga, Mehmet Alper; Demiralp, Metin
    A multivariate interpolation problem is generally constructed for appropriate determination of a multivariate function whose values are given at a finite number of nodes of a multivariate grid. One way to construct the solution of this problem is to partition the given multivariate data into low-variate data. High dimensional model representation (HDMR) and generalized high dimensional model representation (GHDMR) methods are used to make this partitioning. Using the components of the HDMR or the GHDMR expansions the multivariate data can be partitioned. When a cartesian product set in the space of the independent variables is given, the HDMR expansion is used. On the other band, if the nodes are the elements of a random discrete data the GHDMR expansion is used instead of HDMR. These two expansions work well for the multivariate data that have the additive nature. If the data have multiplicative nature then factorized high dimensional model representation (FHDMR) is used. But in most cases the nature of the given multivariate data and the sought multivariate function have neither additive nor multiplicative nature. They have a hybrid nature. So, a new method is developed to obtain better results and it is called hybrid high dimensional model representation (HHDMR). This new expansion includes both the HDMR (or GHDMR) and the FHDMR expansions through a hybridity parameter. In this work, the general structure of this hybrid expansion is given. It has tried to obtain the best value for the hybridity parameter. According to this value the analytical structure of the sought multivariate function can be determined via HHDMR.
  • Küçük Resim
    Yayın
    Bulanık mantık kullanılarak sese duyarlı aydınlatma
    (IEEE, 2017-10-31) Kanburoğlu, Ali Buğra; Şaşmaz, Emre
    Sanayileşmenin ve teknolojinin gelişmesiyle birlikte, geçmişte çözülememiş olan problemler daha kolay çözülebilir hale gelmiştir. İnsan beyninin çalışma mekanizması çeşitli metotlar halinde bilgisayarlarda uygulanmaya başlanmış ve yapay zeka (YZ) alanı ortaya çıkmıştır. YZ tekniklerinin kullanılması ve yaygınlaşmasıyla, bilim dünyasının her alanındaki problemlere çözümler sunulmuştur. Bu çalışmada, YZ’nin tekniklerinden biri olan bulanık mantık (BM) konusu ele alınmıştır. BM kullanılarak, kütüphanelerin ortak alanlarında bulunan aydınlatma sisteminin sese duyarlı bir şekilde modellenmesi gerçekleştirilmiştir.