Sensor Drift Compensation via Olfactory system and Reservoir Computing
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.