Convergence of parameter and delay estimates using open loop time domain gradient methods

Aidan O'Dwyer; John Ringwood

doi:10.21427/s26x-6y22

Convergence of parameter and delay estimates using open loop time domain gradient methods

Aidan O'Dwyer, John Ringwood

Technological University Dublin

Research output: Contribution to conference › Paper › peer-review

Abstract

This paper discusses the estimation of the parameters of a single input, single output (SISO) process, modelled in first order lag plus delay (FOLPD) form, using gradient methods in the open loop time domain. The paper considers the convergence of the process parameters to the model parameters. The convergence of the model delay is discussed first, when the non-delay model and process parameters are identical. The convergence of all of the model parameters is then considered, when all of the process parameters are unknown.

Original language	English
DOIs	https://doi.org/10.21427/s26x-6y22
Publication status	Published - 2000
Event	Irish Signals and Systems Conference - Dublin, Ireland Duration: 1 Jun 2000 → 30 Jun 2000

Conference

Conference	Irish Signals and Systems Conference
Country/Territory	Ireland
City	Dublin
Period	1/06/00 → 30/06/00

Keywords

SISO process
first order lag plus delay (FOLPD)
gradient methods
open loop time domain
convergence
model parameters
model delay
process parameters

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10.21427/s26x-6y22Licence: CC BY-NC-SA

Cite this

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title = "Convergence of parameter and delay estimates using open loop time domain gradient methods",

abstract = "This paper discusses the estimation of the parameters of a single input, single output (SISO) process, modelled in first order lag plus delay (FOLPD) form, using gradient methods in the open loop time domain. The paper considers the convergence of the process parameters to the model parameters. The convergence of the model delay is discussed first, when the non-delay model and process parameters are identical. The convergence of all of the model parameters is then considered, when all of the process parameters are unknown.",

keywords = "SISO process, first order lag plus delay (FOLPD), gradient methods, open loop time domain, convergence, model parameters, model delay, process parameters",

author = "Aidan O'Dwyer and John Ringwood",

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doi = "10.21427/s26x-6y22",

language = "English",

note = "Irish Signals and Systems Conference ; Conference date: 01-06-2000 Through 30-06-2000",

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T1 - Convergence of parameter and delay estimates using open loop time domain gradient methods

AU - O'Dwyer, Aidan

AU - Ringwood, John

PY - 2000

Y1 - 2000

N2 - This paper discusses the estimation of the parameters of a single input, single output (SISO) process, modelled in first order lag plus delay (FOLPD) form, using gradient methods in the open loop time domain. The paper considers the convergence of the process parameters to the model parameters. The convergence of the model delay is discussed first, when the non-delay model and process parameters are identical. The convergence of all of the model parameters is then considered, when all of the process parameters are unknown.

AB - This paper discusses the estimation of the parameters of a single input, single output (SISO) process, modelled in first order lag plus delay (FOLPD) form, using gradient methods in the open loop time domain. The paper considers the convergence of the process parameters to the model parameters. The convergence of the model delay is discussed first, when the non-delay model and process parameters are identical. The convergence of all of the model parameters is then considered, when all of the process parameters are unknown.

KW - SISO process

KW - first order lag plus delay (FOLPD)

KW - gradient methods

KW - open loop time domain

KW - convergence

KW - model parameters

KW - model delay

KW - process parameters

U2 - 10.21427/s26x-6y22

DO - 10.21427/s26x-6y22

M3 - Paper

T2 - Irish Signals and Systems Conference

Y2 - 1 June 2000 through 30 June 2000

ER -

Convergence of parameter and delay estimates using open loop time domain gradient methods

Abstract

Conference

Keywords

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