算法
计算机科学
数学
核(代数)
自适应滤波器
模式识别(心理学)
人工智能
自适应算法
核自适应滤波器
功能(生物学)
最小均方滤波器
作者
Yiming Zhang,Libiao Peng,Xifeng Li,Yongle Xie
出处
期刊:IEEE Signal Processing Letters
[Institute of Electrical and Electronics Engineers]
日期:2020-03-04
卷期号:27: 476-480
被引量:5
标识
DOI:10.1109/lsp.2020.2978408
摘要
In this letter, a novel kernel function named $q$ -Renyi kernel is proposed. Based on it, a new online adaptive learning algorithm is presented, which is derived based on the recursive adaptive filtering paradigm under the reproducing kernel Hilbert space. The proposed learning algorithm is different from the conventional kernel-based learning paradigm in two senses: first, the reproducing kernel so-called $\boldsymbol {q}$ -Renyi kernel is firstly derived and employed; and second, a sparsity constraint is utilized to generate a small size of neural networks while maintaining a high learning performance. The effectiveness of the proposed algorithm is demonstrated via numerical simulations.
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