Membership set identification with periodic inputs and orthonormal regressors

Akçay, Hüseyin; At, Nuray

doi:10.1016/j.sigpro.2006.03.025

Cited by 2 publications

(1 citation statement)

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“…Let N be the number of input u(k) and output y(k) measurements available from the process. From the state-space representation of the orthonormal basis, the vector l(k) in (19) can be calculated for k = 1, …, N. Then, using a set of available I/O measurements and considering an exact representation of the polytope generated by S, one obtains the following: Based on the definition of the set S in (26), the bounds of the uncertain parameters c(ε) can be calculated using different robust identification algorithms found in the literature (Milanese and Belforte, 1982;Mo and Norton, 1990;da Silva, 1995;Akçay and At, 2006). The computation of the polytope generated by S can become complicated as the number N of measurements increases.…”

Section: Robust Identificationmentioning

confidence: 99%

An introduction to models based on Laguerre, Kautz and other related orthonormal functions – part I: linear and uncertain models

Oliveira

Rosa

Campello

et al. 2011

IJMIC

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This paper provides an overview of system identification using orthonormal basis function models, such as those based on Laguerre, Kautz, and generalised orthonormal basis functions. The paper is separated in two parts. In this first part, the mathematical foundations of these models as well as their advantages and limitations are discussed within the context of linear and robust system identification. The second part approaches the issues related with non-linear models. The discussions comprise a broad bibliographical survey of the subjects involving linear models within the orthonormal basis functions framework. Theoretical and practical issues regarding the identification of these models are presented and illustrated by means of a case study involving a polymerisation process.

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Section: Robust Identificationmentioning

confidence: 99%