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New developments in the CAPTAIN Toolbox for Matlab with case study examples

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New developments in the CAPTAIN Toolbox for Matlab with case study examples. / Taylor, C. James; Young, Peter C; Tych, Wlodzimierz; Wilson, Emma Denise.

In: IFAC-PapersOnLine, Vol. 51, No. 15, 2018, p. 694-699.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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@article{685a4d89b5f74fcb9bb3629ef9cc3f99,
title = "New developments in the CAPTAIN Toolbox for Matlab with case study examples",
abstract = "The CAPTAIN Toolbox is a collection of Matlab algorithmic routines for time series analysis, forecasting and control. It is intended for system identification, signal extraction, interpolation, forecasting and control of a wide range of linear and non-linear stochastic systems across science, engineering and the social sciences. This article briefly reviews the main features of the Toolbox, outlines some recent developments and presents a number of examples that demonstrate the performance of these new routines. The examples range from consideration of global climate data, through to electro-mechanical systems and broiler chicken growth rates. The new version of the Toolbox consists of the following three modules that can be installed independently or together: off-line, time-varying parameter estimation routines for Unobserved Component (UC) modelling and forecasting; Refined Instrumental Variable (RIV) algorithms for the identification and estimation of both discrete and hybrid continuous-time transfer function models; and various routines for Non-Minimal State Space (NMSS) feedback control system design. This new segmented approach is designed to provide new users with a gentler introduction to Toolbox functionality; one that focuses on their preferred application area. It will also facilitate more straightforward incorporation of novel algorithms in the future.",
keywords = "Identification, estimation, forecasting, signal processing, control system design, robotic systems, climate data, chicken growth",
author = "Taylor, {C. James} and Young, {Peter C} and Wlodzimierz Tych and Wilson, {Emma Denise}",
year = "2018",
doi = "10.1016/j.ifacol.2018.09.202",
language = "English",
volume = "51",
pages = "694--699",
journal = "IFAC-PapersOnLine",
issn = "2405-8963",
publisher = "IFAC Secretariat",
number = "15",
note = "18th IFAC Symposium on System Identification ; Conference date: 09-07-2018 Through 11-07-2018",

}

RIS

TY - JOUR

T1 - New developments in the CAPTAIN Toolbox for Matlab with case study examples

AU - Taylor, C. James

AU - Young, Peter C

AU - Tych, Wlodzimierz

AU - Wilson, Emma Denise

PY - 2018

Y1 - 2018

N2 - The CAPTAIN Toolbox is a collection of Matlab algorithmic routines for time series analysis, forecasting and control. It is intended for system identification, signal extraction, interpolation, forecasting and control of a wide range of linear and non-linear stochastic systems across science, engineering and the social sciences. This article briefly reviews the main features of the Toolbox, outlines some recent developments and presents a number of examples that demonstrate the performance of these new routines. The examples range from consideration of global climate data, through to electro-mechanical systems and broiler chicken growth rates. The new version of the Toolbox consists of the following three modules that can be installed independently or together: off-line, time-varying parameter estimation routines for Unobserved Component (UC) modelling and forecasting; Refined Instrumental Variable (RIV) algorithms for the identification and estimation of both discrete and hybrid continuous-time transfer function models; and various routines for Non-Minimal State Space (NMSS) feedback control system design. This new segmented approach is designed to provide new users with a gentler introduction to Toolbox functionality; one that focuses on their preferred application area. It will also facilitate more straightforward incorporation of novel algorithms in the future.

AB - The CAPTAIN Toolbox is a collection of Matlab algorithmic routines for time series analysis, forecasting and control. It is intended for system identification, signal extraction, interpolation, forecasting and control of a wide range of linear and non-linear stochastic systems across science, engineering and the social sciences. This article briefly reviews the main features of the Toolbox, outlines some recent developments and presents a number of examples that demonstrate the performance of these new routines. The examples range from consideration of global climate data, through to electro-mechanical systems and broiler chicken growth rates. The new version of the Toolbox consists of the following three modules that can be installed independently or together: off-line, time-varying parameter estimation routines for Unobserved Component (UC) modelling and forecasting; Refined Instrumental Variable (RIV) algorithms for the identification and estimation of both discrete and hybrid continuous-time transfer function models; and various routines for Non-Minimal State Space (NMSS) feedback control system design. This new segmented approach is designed to provide new users with a gentler introduction to Toolbox functionality; one that focuses on their preferred application area. It will also facilitate more straightforward incorporation of novel algorithms in the future.

KW - Identification

KW - estimation

KW - forecasting

KW - signal processing

KW - control system design

KW - robotic systems

KW - climate data

KW - chicken growth

U2 - 10.1016/j.ifacol.2018.09.202

DO - 10.1016/j.ifacol.2018.09.202

M3 - Journal article

VL - 51

SP - 694

EP - 699

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 15

T2 - 18th IFAC Symposium on System Identification

Y2 - 9 July 2018 through 11 July 2018

ER -