UMP Institutional Repository

An oppositional learning prediction operator for simulated kalman filter

Zuwairie, Ibrahim and Kamil Zakwan, Mohd Azmi and Badaruddin, Muhammad and Mohd Falfazli, Mat Jusof and Nor Azlina, Alias and Nor Hidayati, Abdul Aziz and Mohd Ibrahim, Shapiai (2018) An oppositional learning prediction operator for simulated kalman filter. In: 3th International Conference on Computational Intelligence and Applications 2018, 28 - 30 July 2018 , Hong Kong. pp. 1-5.. (Unpublished)

[img] Pdf
9. An Oppostional Learning Prediction Operator For Simulated Kalman Filter.pdf
Restricted to Repository staff only

Download (199kB) | Request a copy
[img]
Preview
Pdf
9.1 An Oppostional Learning Prediction Operator For Simulated Kalman Filter.pdf

Download (10kB) | Preview

Abstract

Simulated Kalman filter (SKF) is a recent metaheuristic optimization algorithm established in 2015. In the present study, we introduce a prediction operator in SKF to prolong its exploration and to avoid premature convergence. The proposed prediction operator is based on oppositional learning. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator outperforms the original SKF algorithm in most cases.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: SKF; Prediction; Oppositional learning
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Faculty of Manufacturing Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 07 Dec 2018 06:54
Last Modified: 07 Dec 2018 06:54
URI: http://umpir.ump.edu.my/id/eprint/22171
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item