Improved model-order reduction by using spacial information in moments
نویسنده
چکیده
The new concept of multinode moment matching (MMM) is introduced in this paper. The MMM technique simultaneously matches the moments at several nodes of a circuit using explicit moment matching around = 0. As compared to the well known single-point moment matching (SMM) techniques (such as asymptotic waveform evaluation), MMM has several advantages. First, the number of moments required by MMM is significantly lower than SMM for a reduced-order model of the same accuracy, which directly translates into computational efficiency. This higher computational efficiency of MMM as compared to SMM increases with the number of inputs to the circuit. Second, MMM has much better numerical stability as compared to SMM. This characteristic allows MMM to calculate an arbitrarily high-order approximation of a linear system, achieving the required accuracy for systems with complex responses. Finally, MMM is highly suitable for parallel-processing techniques especially for higher order approximations while SMM has to calculate the moments sequentially and cannot be adapted to parallel processing techniques.
منابع مشابه
CVaR Reduced Fuzzy Variables and Their Second Order Moments
Based on credibilistic value-at-risk (CVaR) of regularfuzzy variable, we introduce a new CVaR reduction method fortype-2 fuzzy variables. The reduced fuzzy variables arecharacterized by parametric possibility distributions. We establishsome useful analytical expressions for mean values and secondorder moments of common reduced fuzzy variables. The convex properties of second order moments with ...
متن کاملEMPIRE: An Efficient and Compact Multiple-Parameterized Model Order Reduction Method
In physical design and optimization for VLSI/ULSI, parameterized model order reduction can be used to handle large design objectives. In this paper we propose an efficient yet accurate parameterized model order reduction method EMPIRE for physical design with multiple parameters. It is the first practical algorithm using implicit moment matching to handle high order moments of very large number...
متن کاملNumerical Simulation of Air Flow around the NP Car Using the Realizable k-ε Turbulence Model to Predict Aerodynamic Forces and Moments
In this study, a numerical computational fluid dynamics study is conducted in order to predict the aerodynamic forces on the NP car. The turbulent air flow around the car is modeled using the realizable k-ε model. First, results are validated against those presented for the Ahmed’s body. Next, the fluid flow around the car is simulated for different car speeds ( to mph) and fl...
متن کاملبهبود مدل تفکیککننده منیفلدهای غیرخطی بهمنظور بازشناسی چهره با یک تصویر از هر فرد
Manifold learning is a dimension reduction method for extracting nonlinear structures of high-dimensional data. Many methods have been introduced for this purpose. Most of these methods usually extract a global manifold for data. However, in many real-world problems, there is not only one global manifold, but also additional information about the objects is shared by a large number of manifolds...
متن کاملAN Improved UTD Based Model For The Multiple Building Diffraction Of Plane Waves In Urban Environments By Using Higher Order Diffraction Coeficients
This paper describes an improved model for multiple building diffraction modeling based on the uniform theory of diffraction (UTD). A well-known problem in conventional uniform theory of diffraction (CUTD) is multiple-edge transition zone diffraction. Here, higher order diffracted fields are used in order to improve the result; hence, we use higher order diffraction coefficients to improve a hy...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- IEEE Trans. VLSI Syst.
دوره 11 شماره
صفحات -
تاریخ انتشار 2003