English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Gray-box identification is prevalent in modeling physical and networkedsystems. However, due to the non-convex nature of the gray-box identificationproblem, good initial parameter estimates are crucial for a successfulapplication. In this paper, a new identification method is proposed byexploiting the low-rank and structured Hankel matrix of impulse response. Thisidentification problem is recasted into a difference-of-convex programmingproblem, which is then solved by the sequential convex programming approachwith the associated initialization obtained by nuclear-norm optimization. Thepresented method aims to achieve the maximum impulse-response fitting while notrequiring additional (non-convex) conditions to secure non-singularity of thesimilarity transformation relating the given state-space matrices to thegray-box parameterized ones. This overcomes a persistent shortcoming in anumber of recent contributions on this topic, and the new method can be appliedfor the structured state-space realization even if the involved systemparameters are unidentifiable. The method can be used both for directlyestimating the gray-box parameters and for providing initial parameterestimates for further iterative search in a conventional gray-boxidentification setup.

Gray Box Identification of State-Space Models Using Difference of Convex Programming