Parameter tuning in the single-solution simulated Kalman filter optimizer

Nor Hidayati Abdul Aziz; Zuwairie Ibrahim; Nor Azlina Ab Aziz; Badaruddin Muhammad; Tasiransurini Ab Rahman; Mohd Saberi Mohamad; Suhazri Amrin Rahmad

doi:10.1007/978-981-13-9539-0_5

Parameter tuning in the single-solution simulated Kalman filter optimizer

Nor Hidayati Abdul Aziz
, Zuwairie Ibrahim
, Nor Azlina Ab Aziz
, Badaruddin Muhammad
, Tasiransurini Ab Rahman
, Mohd Saberi Mohamad
, Suhazri Amrin Rahmad

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

Single-solution simulated Kalman filter (ssSKF) is a variant of simulated Kalman filter (SKF) algorithm. Both algorithms employ the well-known Kalman filtering mechanism in an optimization process. Unlike the population-based SKF, the ssSKF operates using one agent. In this paper, parameter tuning of the ssSKF algorithm is presented.

Original language	English
Pages (from-to)	48-56
Number of pages	9
Journal	Lecture Notes in Mechanical Engineering
DOIs	https://doi.org/10.1007/978-981-13-9539-0_5
Publication status	Published - 2020
Externally published	Yes
Event	2nd Symposium on Intelligent Manufacturing and Mechatronics, SympoSIMM 2019 - Melaka, Malaysia Duration: Jul 8 2019 → Jul 8 2019

Keywords

Optimization
Simulated Kalman filter

ASJC Scopus subject areas

Automotive Engineering
Aerospace Engineering
Mechanical Engineering
Fluid Flow and Transfer Processes

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10.1007/978-981-13-9539-0_5

Cite this

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title = "Parameter tuning in the single-solution simulated Kalman filter optimizer",

abstract = "Single-solution simulated Kalman filter (ssSKF) is a variant of simulated Kalman filter (SKF) algorithm. Both algorithms employ the well-known Kalman filtering mechanism in an optimization process. Unlike the population-based SKF, the ssSKF operates using one agent. In this paper, parameter tuning of the ssSKF algorithm is presented.",

keywords = "Optimization, Simulated Kalman filter",

author = "\{Abdul Aziz\}, \{Nor Hidayati\} and Zuwairie Ibrahim and \{Ab Aziz\}, \{Nor Azlina\} and Badaruddin Muhammad and \{Ab Rahman\}, Tasiransurini and Mohamad, \{Mohd Saberi\} and Rahmad, \{Suhazri Amrin\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.; 2nd Symposium on Intelligent Manufacturing and Mechatronics, SympoSIMM 2019 ; Conference date: 08-07-2019 Through 08-07-2019",

year = "2020",

doi = "10.1007/978-981-13-9539-0\_5",

language = "English",

pages = "48--56",

journal = "Lecture Notes in Mechanical Engineering",

issn = "2195-4356",

publisher = "Springer Verlag",

}

TY - JOUR

T1 - Parameter tuning in the single-solution simulated Kalman filter optimizer

AU - Abdul Aziz, Nor Hidayati

AU - Ibrahim, Zuwairie

AU - Ab Aziz, Nor Azlina

AU - Muhammad, Badaruddin

AU - Ab Rahman, Tasiransurini

AU - Mohamad, Mohd Saberi

AU - Rahmad, Suhazri Amrin

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2020.

PY - 2020

Y1 - 2020

N2 - Single-solution simulated Kalman filter (ssSKF) is a variant of simulated Kalman filter (SKF) algorithm. Both algorithms employ the well-known Kalman filtering mechanism in an optimization process. Unlike the population-based SKF, the ssSKF operates using one agent. In this paper, parameter tuning of the ssSKF algorithm is presented.

AB - Single-solution simulated Kalman filter (ssSKF) is a variant of simulated Kalman filter (SKF) algorithm. Both algorithms employ the well-known Kalman filtering mechanism in an optimization process. Unlike the population-based SKF, the ssSKF operates using one agent. In this paper, parameter tuning of the ssSKF algorithm is presented.

KW - Optimization

KW - Simulated Kalman filter

UR - https://www.scopus.com/pages/publications/85071310003

UR - https://www.scopus.com/pages/publications/85071310003#tab=citedBy

U2 - 10.1007/978-981-13-9539-0_5

DO - 10.1007/978-981-13-9539-0_5

M3 - Conference article

AN - SCOPUS:85071310003

SN - 2195-4356

SP - 48

EP - 56

JO - Lecture Notes in Mechanical Engineering

JF - Lecture Notes in Mechanical Engineering

T2 - 2nd Symposium on Intelligent Manufacturing and Mechatronics, SympoSIMM 2019

Y2 - 8 July 2019 through 8 July 2019

ER -

Parameter tuning in the single-solution simulated Kalman filter optimizer

Abstract

Keywords

ASJC Scopus subject areas

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