صورة الغلاف

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...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Zuwairie, Ibrahim, Kamil Zakwan, Mohd Azmi, Badaruddin, Muhammad, Mohd Falfazli, Mat Jusof, Nor Azlina, Alias, Nor Hidayati, Abdul Aziz, Mohd Ibrahim, Shapiai
التنسيق: Conference or Workshop Item
اللغة:English
English
منشور في: 2018
الموضوعات:
TK Electrical engineering. Electronics Nuclear engineering
الوصول للمادة أونلاين: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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الوصف
الملخص: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.

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