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A generalized maximum correntropy based multi-state recurrent kernel online learning algorithm
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AI
DOI：10.1016/j.sigpro.2026.110878.png)
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Abstract
En 中文
• We proposed a novel state feedback structure utilizing the state information of the previous multiple moments.
• Based on the state feedback structure and GMC, we proposed a multi-state recurrent GMC (MSRKGMC) algorithm for kernel online learning.
• The convergence analysis is conducted to control the evolution of weight parameters.
• The convergence analysis is conducted to control the evolution of weight parameters.
Keywords:
Kernel adaptive filtering
Generalized maximum correntropy
Multi-state recurrent
Non-Gaussian noise
Convergence analysis
Journal
IF:
3.6
Papers:
9.9K
Citations:
1.7W
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