Identification of ARX and ARARX Models in the Presence of Input and Output Noises

Diversi, Roberto; Guidorzi, Roberto; Soverini, Umberto

doi:10.3166/ejc.16.242-255

Cited by 50 publications

(41 citation statements)

References 14 publications

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“…leading to positive semidefinite matricesΣ(P ) = Σ −Σ(P ) with one eigenvalue equal to zero. This set is decribed by the following results, which can be proved along the same lines considered in [18].…”

Section: A First Step: a Frisch Scheme-based Methods 1) Some Asymptotmentioning

confidence: 98%

“…2) The locus of solutions of the Frisch scheme: The identification of MISO noisy I/O ARX models can be mapped into an errors-in-variables identification problem in the Frisch scheme context by extending the results described in [18] that concern the SISO case. To this end, consider the problem of determining the family of all non-negative definite diagonal matricesΣ of type (19) that subtracted to Σ lead to positive semidefinite matrices, that is the set of matrices of typẽ…”

Section: A First Step: a Frisch Scheme-based Methods 1) Some Asymptotmentioning

confidence: 99%

“…The basic idea of the Frisch scheme is to search for the solution of the identification problem within a locus of solutions which are compatible with the second order statistics of the noisy data [19]. To select a single solution among all compatible ones a suitable criterion must be introduced [18]- [21]. The selection criterion considered in this paper is based on a set of both low-order and high-order Yule-Walker equations.…”

Section: Introductionmentioning

confidence: 99%

“…With this setting, the related identification problem can be mapped into an Errors-In-Variables (EIV) framework [17], and we propose to solve it by a an ad-hoc two-step algorithm. In the first step, a solution based on the Frisch scheme is derived from [18], in order to manage input and output noises. The basic idea of the Frisch scheme is to search for the solution of the identification problem within a locus of solutions which are compatible with the second order statistics of the noisy data [19].…”

Section: Introductionmentioning

confidence: 99%

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Identification of many-core systems-on-chip with input and output noises

Diversi

Tilli

Bartolini

et al. 2013

52nd IEEE Conference on Decision and Control

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Dynamic optimization of computing performance under thermal constraints is expected to be a cornerstone in next generation many-core systems-on-chip. Toward this goal, compact, scalable and accurate thermal models are crucial. In this work, a two-step identification procedure is presented to derive a set of local, yet interconnected, thermal models which are suitable for distributed control. The case of very noisy temperature measurement available on each core is considered, as it is the most commonly encountered in reallife multicore chips. The first identification step is based on a MISO Frisch scheme to deal with both input and output noises. Then, exploiting physical insight on the characteristics of measurement noises, a second ad-hoc procedure is proposed to refine the identified model. The proposed solution has been successfully applied to an Intel's Single-chip-Cloud-Computer (SCC), a prototype with 48 cores.

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Section: A First Step: a Frisch Scheme-based Methods 1) Some Asymptotmentioning

confidence: 98%

Section: A First Step: a Frisch Scheme-based Methods 1) Some Asymptotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of many-core systems-on-chip with input and output noises

Diversi

Tilli

Bartolini

et al. 2013

52nd IEEE Conference on Decision and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is then possible to proceed as above with ρ =  λ yu λ u  T and using only delayed input values in the vector z(t). Such a case is treated in Diversi, Guidorzi, and Soverini (2010).…”

Section: Special Cases and Extensionsmentioning

confidence: 98%

A unified framework for EIV identification methods when the measurement noises are mutually correlated

2014

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Bias‐eliminating least‐squares identification of errors‐in‐variables models with mutually correlated noises

Diversi

2012

Adaptive Control & Signal

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SUMMARYThis paper proposes a bias‐eliminating least‐squares (BELS) approach for identifying linear dynamic errors‐in‐variables (EIV) models whose input and output are corrupted by additive white noise. The method is based on an iterative procedure involving, at each step, the estimation of both the system parameters and the noise variances. The proposed identification algorithm differs from previous BELS algorithms in two aspects. First, the input and output noises are allowed to be mutually correlated, and second, the estimation of the noise covariances is obtained by exploiting the statistical properties of the equation error of the EIV model. Copyright © 2012 John Wiley & Sons, Ltd.

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Identification of ARX and ARARX Models in the Presence of Input and Output Noises

Cited by 50 publications

References 14 publications

Identification of many-core systems-on-chip with input and output noises

Identification of many-core systems-on-chip with input and output noises

A unified framework for EIV identification methods when the measurement noises are mutually correlated

Bias‐eliminating least‐squares identification of errors‐in‐variables models with mutually correlated noises

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