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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| 主要な著者: | , , , , , , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English English |
| 出版事項: |
2018
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| 主題: | |
| オンライン･アクセス: | 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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http://umpir.ump.edu.my/id/eprint/22171/1/9.%20An%20Oppostional%20Learning%20Prediction%20Operator%20For%20Simulated%20Kalman%20Filter.pdfhttp://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/