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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper considers the problem of system identification for linear systems.We propose a new system realization approach that uses an ``information-state"as the state vector, where the ``information-state" is composed of a finitenumber of past inputs and outputs. The system identification algorithm usesinput-output data to fit an autoregressive moving average model (ARMA) torepresent the current output in terms of finite past inputs and outputs. Thisinformation-state-based approach allows us to directly realize a state-spacemodel using the estimated ARMA or time-varying ARMA parameters for linear timeinvariant (LTI) or linear time varying (LTV) systems, respectively. The paperdevelops the theoretical foundation for using ARMA parameters-based systemrepresentation using only the concept of linear observability, details thereasoning for exact output modeling using only the finite history, and showsthat there is no need to separate the free and the forced response foridentification. The proposed approach is tested on various different systems,and the performance is compared with state-of-the-art system identificationtechniques.

The Information-State Based Approach to Linear System Identification