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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2019
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| Subjects: | |
| 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 |
Internet
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