Causal and stable reduced-order model for linear high-frequency systems

With the ever-growing complexity of high-frequency systems in the electronic industry, formation of reduced-order models of these systems is paramount. In this reported work, two different techniques are combined to generate a stable and causal representation of the system. In particular, balanced truncation is combined with a Fourier series expansion approach. The efficacy of the proposed combined method is shown with an example

Interpolatory methods for H∞\mathcal{H}_\infty model reduction of multi-input/multi-output systems

We develop here a computationally effective approach for producing high-quality $\mathcal{H}_\infty$-approximations to large scale linear dynamical systems having multiple inputs and multiple outputs (MIMO). We extend an approach for $\mathcal{H}_\infty$ model reduction introduced by Flagg, Beattie, and Gugercin for the single-input/single-output (SISO) setting, which combined ideas originating in interpolatory $\mathcal{H}_2$-optimal model reduction with complex Chebyshev approximation. Retaining this framework, our approach to the MIMO problem has its principal computational cost dominated by (sparse) linear solves, and so it can remain an effective strategy in many large-scale settings. We are able to avoid computationally demanding $\mathcal{H}_\infty$ norm calculations that are normally required to monitor progress within each optimization cycle through the use of "data-driven" rational approximations that are built upon previously computed function samples. Numerical examples are included that illustrate our approach. We produce high fidelity reduced models having consistently better $\mathcal{H}_\infty$ performance than models produced via balanced truncation; these models often are as good as (and occasionally better than) models produced using optimal Hankel norm approximation as well. In all cases considered, the method described here produces reduced models at far lower cost than is possible with either balanced truncation or optimal Hankel norm approximation

Adaptive classification algorithm for EMC-compliance testing of electronic devices

A novel technique that facilitates near-field (NF) scanning for electromagnetic compatibility-compliance testing is described. It performs measurements in a sequential way with the aim of discovering multiple, possibly disjoint regions where the amplitudes of an NF component belong to certain output ranges. The measured data samples are used to train a classification model where each NF range is represented by a given class (e.g. low/medium/high NF amplitudes). The outcome of the algorithm is a visual map that clearly characterises and pinpoints the exact location and boundaries of each class. Such maps are useful, for example, to detect hotspots or regions that are prone to electromagnetic compatibility issues. The technique has been validated on a measured microstrip bend discontinuity

Combining Krylov subspace methods and identification-based methods for model order reduction

Many different techniques to reduce the dimensions of a model have been proposed in the near past. Krylov subspace methods are relatively cheap, but generate non-optimal models. In this paper a combination of Krylov subspace methods and orthonormal vector fitting (OVF) is proposed. In that way a compact model for a large model can be generated. In the first step, a Krylov subspace method reduces the large model to a model of medium size, then a compact model is derived with OVF as a second step