用深度神经网络直接学习滤波梯度，提升自适应滤波泛化能力。
Deep Neural Network-Driven Adaptive Filtering
- DNN嵌入滤波核心，直接映射残差到梯度
- 基于最大似然隐式损失，数据驱动性能优异
- 适用于非高斯场景，稳定性分析完备
本文提出一种基于深度神经网络（DNN）的自适应滤波框架，解决传统方法在泛化性上的长期挑战。与强调显式代价函数设计的传统框架不同，该框架转向直接获取梯度。DNN作为通用非线性算子，被结构化嵌入自适应滤波系统核心，建立滤波残差与学习梯度间的直接映射。采用最大似然作为隐式代价函数，使算法天然具备数据驱动特性，展现出卓越的泛化能力。通过在多种非高斯场景下的大量数值实验验证了其有效性。同时，对算法均值和均方稳定性进行了详尽分析。
原文摘要 · Abstract (English)
This paper proposes a deep neural network (DNN)-driven framework to address the longstanding generalization challenge in adaptive filtering (AF). In contrast to traditional AF frameworks that emphasize explicit cost function design, the proposed framework shifts the paradigm toward direct gradient acquisition. The DNN, functioning as a universal nonlinear operator, is structurally embedded into the core architecture of the AF system, establishing a direct mapping between filtering residuals and learning gradients. The maximum likelihood is adopted as the implicit cost function, rendering the derived algorithm inherently data-driven and thus endowed with exemplary generalization capability, which is validated by extensive numerical experiments across a spectrum of non-Gaussian scenarios. Corresponding mean value and mean square stability analyses are also conducted in detail.
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