Oppositional learning prediction operator with jumping rate for simulated kalman filter

Oppositional learning prediction operator with jumping rate for simulated kalman filter

Simulated Kalman filter (SKF) is among the new generation of metaheuristic optimization algorithm established in 2015. In this 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 lea...

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Bibliographic Details
Main Authors: Badaruddin, Muhammad, Mohd Saberi, Mohamad, Zuwairie, Ibrahim, Kamil Zakwan, Mohd Azmi, Mohd Ibrahim, Shapiai, Mohd Falfazli, Mat Jusof
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2019
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
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Online Access:http://umpir.ump.edu.my/id/eprint/25147/
http://umpir.ump.edu.my/id/eprint/25147/
http://umpir.ump.edu.my/id/eprint/25147/1/43.%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf
http://umpir.ump.edu.my/id/eprint/25147/2/43.1%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf

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