Optimum Order Estimation of Reduced Macromodels Based on a Geometric Approach for Projection-Based MOR Methods

被引：6

作者：

Nouri, Behzad ^{[1
]}

Nakhla, Michel S. ^{[1
]}

Achar, Ramachandra ^{[1
]}

机构：

[1] Carleton Univ, Dept Elect, Ottawa, ON K1S 5B6, Canada

来源：

IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY | 2013年 / 3卷 / 07期

关键词：

False nearest neighbor (FNN); linear network analysis; macromodeling; neighborhood range; order estimation; parallel processing; projection; reduced-order models; NEAREST NEIGHBORS METHOD; ALGORITHM;

D O I：

10.1109/TCPMT.2013.2259167

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Estimation of the optimal order of reduced models in existing macromodeling techniques is a challenging task and is often based on heuristics. In this paper, a new algorithm is described for estimating the minimum acceptable order for reduced models of linear systems to ensure accurate as well as efficient transient behavior. The precise determination of the optimum order for a reduced system is based on evaluation of the number of false nearest neighbors.

引用

页码：1218 / 1227

页数：10

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