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Yayın Immitance data modelling via linear interpolation techniques: a classical circuit theory approach(Wiley-Blackwell, 2004-11) Yarman, Bekir Sıddık Binboğa; Kılınç, Ali; Aksen, AhmetWith the advancement of the manufacturing technologies to produce new generation analog/digital communication systems, immitance data modelling has gained renewed importance in the literature. Specifically, models are utilized for behaviour characterization, simulation of physical devices or to design sub-systems with active and passive solid-state devices. Therefore, in this paper, new computer aided tools are presented to model one port immitance data by means of linear interpolation techniques. The basic philosophy of the new modelling tools is based on the numerical decomposition of the immitance data into its minimum and Foster parts. Computer algorithms are presented to model the minimum and the Foster parts of the given immitance data. Implementations of these algorithms are exhibited by means of examples. Depending on the application, modelling tools based on linear interpolation techniques may present 'computational and practical' advantages over the existing interpolation techniques, non-linear curve fittings or regression methods. It is expected that the new modelling tools will be utilized to provide initial circuit topologies to the commercially available analysis/simulation and design packages.Yayın Hybrid high dimensional model representation (HHDMR) on the partitioned data(Elsevier B.V., 2006-01-01) Tunga, Mehmet Alper; Demiralp, MetinA 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.
