實物特徵: An oppositional learning prediction operator for simulated kalman filter

An oppositional learning prediction operator for simulated kalman filter

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 r...

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書目詳細資料
Main Authors:	Zuwairie, Ibrahim, Kamil Zakwan, Mohd Azmi, Badaruddin, Muhammad, Mohd Falfazli, Mat Jusof, Nor Azlina, Alias, Nor Hidayati, Abdul Aziz, Mohd Ibrahim, Shapiai
格式:	Conference or Workshop Item
語言:	English English
出版:	2018
主題:	TK Electrical engineering. Electronics Nuclear engineering
在線閱讀:	http://umpir.ump.edu.my/id/eprint/22171/1/9.%20An%20Oppostional%20Learning%20Prediction%20Operator%20For%20Simulated%20Kalman%20Filter.pdf http://umpir.ump.edu.my/id/eprint/22171/2/9.1%20An%20Oppostional%20Learning%20Prediction%20Operator%20For%20Simulated%20Kalman%20Filter.pdf http://umpir.ump.edu.my/id/eprint/22171/
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實物特徵
總結:	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.

An oppositional learning prediction operator for simulated kalman filter

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