RBF-Based Weighted Minimum Likelihood Error Entropy Against Multimodal Noise

The performance of model-based (MB) robust adaptive filters is highly sensitive to specific noise distributions and significantly degrades under complex multi-peak noise conditions. To this end, utilizing the universal function approximation capability and high training efficiency of radial basis function (RBF), this letter proposes a novel RBF neural network-assisted minimum likelihood error entropy (MLEE) criterion. To accurately estimate the gradient contribution of previous estimation errors to the current one, a weighted method is constructed in MLEE to improve its convergence rate, ultimately generating RBF-WMLEE algorithm. Simulations on system identification validate the superiorities of proposed RBF-WMLEE under complex multimodal noise conditions.