{
  "annotation_id": "72c5d493-088f-4760-b390-b79818792580",
  "date_created": "2026-02-17T05:53:24.411000Z",
  "date_modified": "2026-02-17T05:53:24.411000Z",
  "file_hash": "d935306a674f2eeb9440e03d86139d57b5cfd20c3716fbb6aefa358ae3d9eebc",
  "private": false,
  "record": {
    "abstract": "Building upon the mean p-power error (MPE) criterion, the normalized subband p-norm (NSPN) algorithm demonstrates superior robustness in $\\alpha$-stable noise environments ($1 \u003c \\alpha \\leq 2$) through effective utilization of low-order moment hidden in robust loss functions. Nevertheless, its performance degrades significantly when processing noise input or additive noise characterized by $\\alpha$-stable processes ($0 \u003c \\alpha \\leq 1$). To overcome these limitations, we propose a novel fractional-order NSPN (FoNSPN) algorithm that incorporates the fractional-order stochastic gradient descent (FoSGD) method into the MPE framework. Additionally, this paper also analyzes the convergence range of its step-size, the theoretical domain of values for the fractional-order $\\beta$, and establishes the theoretical steady-state mean square deviation (MSD) model. Simulations conducted in diverse impulsive noise environments confirm the superiority of the proposed FoNSPN algorithm against existing state-of-the-art algorithms.",
    "arxiv_id": "2601.10074",
    "authors": [
      "Jianhong Ye",
      "Haiquan Zhao",
      "Yi Peng"
    ],
    "categories": [
      "eess.SP"
    ],
    "doi": "10.1109/LSP.2025.3642765",
    "journal_ref": "IEEE Signal Processing Letters, vol. 33, pp. 281 - 285, December 2025",
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "P-norm based Fractional-Order Robust Subband Adaptive Filtering Algorithm for Impulsive Noise and Noisy Input",
    "url": "https://arxiv.org/abs/2601.10074",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a181f2f9-6d10-4d59-9fb4-8937f6f2e165",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}