Temporal data classification and forecasting using a memristor-based reservoir computing system

摘要

Time-series analysis including forecasting is essential in a range of fields from finance to engineering. However, long-termforecasting is difficult, particularly for cases where the underlying models and parameters are complex and unknown. Neuralnetworks can effectively process features in temporal units and are attractive for such purposes. Reservoir computing, in particular,can offer efficient temporal processing of recurrent neural networks with a low training cost, and is thus well suited totime-series analysis and forecasting tasks. Here, we report a reservoir computing hardware system based on dynamic tungstenoxide (WO_x) memristors that can efficiently process temporal data. The internal short-term memory effects of the WO_xmemristors allow the memristor-based reservoir to nonlinearly map temporal inputs into reservoir states, where the projectedfeatures can be readily processed by a linear readout function. We use the system to experimentally demonstrate two standardbenchmarking tasks: isolated spoken-digit recognition with partial inputs, and chaotic system forecasting. A high classificationaccuracy of 99.2% is obtained for spoken-digit recognition, and autonomous chaotic time-series forecasting has been demonstratedover the long term.