Mixture of set membership filters approach for big data signal processing
| Autor: | Suleyman S. Kozat, M. Omer Sayin, O. Fatih Kilic, Ibrahim Delibalta |
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| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
Signal processing Affine combination business.industry Big data 020206 networking & telecommunications 02 engineering and technology Convex combination Computational efficiency Adaptive filter Set (abstract data type) Set-membership filtering Rate of convergence Mixture of experts 0202 electrical engineering electronic engineering information engineering business Algorithm Mathematics |
| Zdroj: | SIU Proceedings of the IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016 |
| DOI: | 10.1109/siu.2016.7495965 |
| Popis: | Date of Conference: 16-19 May 2016 Conference Name: IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016 In this work, we propose a new approach for mixture of adaptive filters based on set-membership filters (SMF) which is specifically designated for big data signal processing applications. By using this approach, we achieve significantly reduced computational load for the mixture methods with better performance in convergence rate and steady-state error with respect to conventional mixture methods. Finally, we approve these statements with the simulations done on produce data. |
| Databáze: | OpenAIRE |
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