AI 中文总结
该研究针对电子鼻传感器漂移问题，提出一种基于SNN特征适配与SRC分类的逐样本在线漂移补偿方法，可应对多种漂移模式，在真实数据集上分类精度优于基线方法。
AI 中文摘要
尽管电子鼻（e-Noses）在医学诊断和工业过程控制中具有广阔应用前景，但传感器漂移仍是一项关键挑战，它会导致传感器响应发生逐渐偏移，从而降低长期传感可靠性。传统漂移补偿方法通常针对批量学习设计，缺乏在非平稳环境中支持连续在线学习的能力。尽管最近已提出几种在线漂移补偿方法，但它们主要基于准在线小批量学习进行分布适配，而不依赖缓冲的真正逐样本在线学习在很大程度上仍未被探索。为解决这些问题，本文提出一种基于脉冲神经网络（SNN）用于特征适配和脉冲储备池计算（SRC）用于分类的逐样本在线漂移补偿方法。通过利用脉冲时序依赖可塑性（STDP），SNN自组织时空吸引子动力学以实现无标签特征适配（STDP-FA），适配后的特征由SRC分类，该分类由胜者通吃（WTA）竞争驱动自监督适配。所提方法可应对多种概念漂移模式，包括渐进漂移、随机漂移、传感器故障和突变。在真实世界传感器漂移数据集上的仿真表明，与基线方法相比，分类精度有明显提升。
英文摘要
Despite the promising applications of electronic noses (e-Noses) in medical diagnosis and industrial process control, sensor drift remains a critical challenge that degrades long-term sensing reliability by inducing gradual shifts in sensor responses. Conventional drift compensation methods are typically designed for batch learning and lack the ability to support continuous online learning in non-stationary environments. Although several online drift compensation methods have recently been proposed, they are mainly based on quasi-online mini-batch learning for distribution adaptation, while true sample-wise online learning without buffering remains largely unexplored. To address these issues, this paper proposes a sample-wise online drift compensation method based on spiking neural networks (SNNs) for feature adaptation and spiking reservoir computing (SRC) for classification. By exploiting spike-timing-dependent plasticity (STDP), the SNN self-organizes spatiotemporal attractor dynamics for label-free feature adaptation (STDP-FA), and the adapted features are classified by SRC with self-supervised adaptation driven by winner-take-all (WTA) competition. The proposed method addresses various concept drift patterns, including gradual drift, random drift, sensor failures, and abrupt changes. Simulations on a real-world sensor drift dataset demonstrate a clear improvement in classification accuracy over baseline methods.
CommentsAuthorship and Version Note. This revised preprint is based on the paper accepted at ICANN 2026. Chenwei Li and Takeaki Yajima are included as co-authors for their substantial contributions; they were omitted from the conference submission due to an administrative error. All authors approved this version. The ICANN 2026/Springer version of record remains unchanged
