Data exploration with reflective adaptive models

Beyer, Uwe; Śmieja, F. J.

doi:10.1016/0167-9473(95)00048-8

Cited by 28 publications

(16 citation statements)

References 10 publications

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“…More recently, the application of adaptive sampling algorithms for EM based modeling has emerged as an active area of research. Investigations in this area have led to adaptive frequency sampling algorithms based on multinomials [3] and rational interpolants [4], as well as novel concepts such as error-based sampling [5] and automatic model generation [6]. For instance, a rational function based model of a microstrip filter is presented in [7].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Adaptive Frequency Sampling for RF and Microwave CAD

Kashi

Devabhaktuni

2006

2006 European Microwave Conference

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In this paper, an enhanced adaptive frequency sampling algorithm for robust electromagnetic (EM) modeling and computer aided design (CAD) is proposed. The algorithm is based on rational interpolation, which leads to more accurate RF/microwave models as compared to other existing interpolants, e.g. spline. Starting with a minimal number of support points i.e. EM data and with lowest-order rational functions, the algorithm systematically produces a rational function model, which meets a user-specified accuracy. In each stage of the algorithm, additional poles and/or zeros are added, and new support points are adaptively selected based on a statistical criterion. The algorithm distinguishes critical regions of an EM component's behavior that need to be densely sampled and uses relatively fewer data in other regions. The advantages of the proposed algorithm are shown via RF/microwave examples.

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Section: Introductionmentioning

confidence: 99%

Enhanced Adaptive Frequency Sampling for RF and Microwave CAD

Kashi

Devabhaktuni

2006

2006 European Microwave Conference

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“…The process of selecting expansion points and building the model in an adaptive way is referred to as reflective exploration (Beyer andŚmieja, 1996). Reflective exploration is an effective technique when its very expensive to obtain the model from Electromagnetic (EM) simulators.…”

Section: Reflective Explorationmentioning

confidence: 99%

“…The expansion points are selected adaptively using a reflective exploration technique. It is a sequential sampling algorithm, where the model is improved incrementally using the best possible data at every time step with additional properties allowing it to propose candidate exploration points (Beyer andŚmieja, 1996). An error-based exploration is implemented to find the expansion points.…”

Section: Introductionmentioning

confidence: 99%

Passivity Preserving Multipoint Model Order Reduction using Reflective Exploration

Samuel

Knockaert

Dhaene

2014

Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics

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Abstract:Reduced state-space models obtained by model order reduction methods must be accurate over the whole frequency range of interest and must also preserve passivity. In this paper, we propose multipoint reduction technique using reflective exploration for adaptively choosing the expansion points. The projection matrices obtained from the expansion points are merged to form the overall projection matrix. In order to obtain a more compact model the projection matrix is truncated based on its singular values. Finally, the reduced order model is obtained, while ensuring that the passivity of the reduced system is preserved during the reduction process.

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“…The location of new frequency samples is determined by minimizing the maximum relative fitting errors of the best models with respect to the frequency. This process, called reflective exploration [62], is iteratively repeated until the largest mismatch of the response is within a predefined tolerance level, and each rule H i is satisfied. If the impedance matrix contains multiple elements, then an optimal data sample are selected for the least converged matrix element.…”

Section: ) Adaptive Modeling Loopmentioning

confidence: 99%

Broadband Rational Macromodeling Based on the Adaptive Frequency Sampling Algorithm and the Partial Element Equivalent Circuit Method

Antonini

Deschrijver

Dhaene

2008

IEEE Trans. Electromagn. Compat.

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Abstract-The increasing operating frequencies in modern designs call for broadband macromodeling techniques. The problem of computing high-accuracy simulation models for high-speed interconnects is of great importance in the modeling arena. Nowadays, many full-wave numerical techniques are available that provide high accuracy, often at a significant cost in terms of memory storage and computing time. Furthermore, designers are usually only interested in a few electrical quantities such as port voltages and currents. So, model order reduction techniques are commonly used to achieve accurate results in a reasonable time. This paper presents a new technique, based on the partial element equivalent circuit method, which allows to generate reduced-order models by adaptively selecting the complexity (order) of the macromodel and suitable frequency samples. Thus, the proposed algorithm allows to limit the computing time while preserving the accuracy. Validation examples are given.Index Terms-Adaptive frequency sampling (AFS), electromagnetic transient analysis, fitting techniques, frequency response, partial element equivalent circuit (PEEC) method.

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Data exploration with reflective adaptive models

Cited by 28 publications

References 10 publications

Enhanced Adaptive Frequency Sampling for RF and Microwave CAD

Enhanced Adaptive Frequency Sampling for RF and Microwave CAD

Passivity Preserving Multipoint Model Order Reduction using Reflective Exploration

Broadband Rational Macromodeling Based on the Adaptive Frequency Sampling Algorithm and the Partial Element Equivalent Circuit Method

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