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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper newly proposes the robust RLS Wi ener FIR prediction algorit hm based on th e innovation theory for t he linear stochastic sy stems including with param eters. In the robu st RLS Wien er predictor, the following information is used. (1) The system matrices for the signal an d the d egraded signal. (2) The observation m atrices for the signal and the degraded signal. (3) The variance of the state for the degraded signal. (4) The cross-variance of the state for the signal with the state. (5) The variance of the observation noise. As a step to obtain the robust RLS Wiener FIR prediction algorithm, this paper presents the robust prediction algorithm of t he signal using the covariance infor mation etc. In the predictor, the following informati on is used. (1) The observation m atrices for the signal a nd the degra ded signal. (2) The variance of the state for the deg raded signal. (3) The auto-covariance inform ation of the state for the degraded signal. (4) The cross-covarian ce information of the state for the signal with that for the degraded signal. (5) The variance of t he observation noise. The estimation accuracy of the proposed robust RLS Wiener FIR predictor is superior to the existing RLS Wiener FIR predictor. Key–Words: Robust RLS Wiener FIR predictor, discrete-time stochastic systems, auto-covariance function, innovation approach, uncertain parameters Received: February 24, 2020. Revised: April 2 , 2020. Re-revised: April 8 , 2020. Accepted: April 17, 2020. Published: April 24, 2020

Robust Recursive Least-Squares Finite Impulse Response Predictor in Linear Discrete-Time Stochastic Systems with Uncertain Parameters