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

We consider the identification of large-scale linear and stable dynamicsystems whose outputs may be the result of many correlated inputs. Hence,severe ill-conditioning may affect the estimation problem. This is a scenariooften arising when modeling complex physical systems given by theinterconnection of many sub-units where feedback and algebraic loops can beencountered. We develop a strategy based on Bayesian regularization where anyimpulse response is modeled as the realization of a zero-mean Gaussian process.The stable spline covariance is used to include information on smoothexponential decay of the impulse responses. We then design a new Markov chainMonte Carlo scheme that deals with collinearity and is able to efficientlyreconstruct the posterior of the impulse responses. It is based on a variationof Gibbs sampling which updates possibly overlapping blocks of the parameterspace on the basis of the level of collinearity affecting the different inputs.Numerical experiments are included to test the goodness of the approach wherehundreds of impulse responses form the system and inputs correlation may bevery high.

Dealing with collinearity in large-scale linear system identification using Bayesian regularization