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

This paper proposes a data-driven set-based estimation algorithm for a classof nonlinear systems with polynomial nonlinearities. Using the system'sinput-output data, the proposed method computes a set that guarantees theinclusion of the system's state in real-time. Although the system is assumed tobe a polynomial type, the exact polynomial functions, and their coefficientsare assumed to be unknown. To this end, the estimator relies on offline andonline phases. The offline phase utilizes past input-output data to estimate aset of possible coefficients of the polynomial system. Then, using thisestimated set of coefficients and the side information about the system, theonline phase provides a set estimate of the state. Finally, the proposedmethodology is evaluated through its application on SIR (Susceptible, Infected,Recovered) epidemic model.

Data-driven Set-based Estimation of Polynomial Systems with Application to SIR Epidemics