Adaptive kernel bandwidth method for kernel normalized LMS adaptive algorithm
| Autor: | Henri George Coanda, Kiyoshi Nishikawa, Dinu Coltuc, Felix Albu |
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| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Adaptive algorithm Computer science 020206 networking & telecommunications 02 engineering and technology Adaptive filter 030507 speech-language pathology & audiology 03 medical and health sciences symbols.namesake Kernel method Variable kernel density estimation Radial basis function kernel 0202 electrical engineering electronic engineering information engineering Kernel adaptive filter Gaussian function symbols Kernel smoother 0305 other medical science Algorithm |
| Zdroj: | 2017 21st International Conference on System Theory, Control and Computing (ICSTCC). |
| DOI: | 10.1109/icstcc.2017.8107080 |
| Popis: | In this paper, an adaptive adjustment method for the kernel parameter used in the kernel adaptive filters (KAFs) is proposed. The KAF is one of the linear-in-the-parameters (LIP) nonlinear filters, and is based on the kernel method used in machine learning. Typically, the Gaussian kernel function is used, but there is no effective method for automatically adjusting its parameter that influences the convergence characteristics of the KAFs. An adaptive adjustment method for this parameter is proposed in the paper. The proposed method uses the difference of l 1 norms of the input signals for the unknown system and the adaptive filter as the criteria. The kernel parameter will be updated according to the differences. The qualitative results of the proposed method is shown by the computer simulations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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