Reservoir computing

Aerospace engineering

Pattern recognition (psychology)

Block (permutation group theory)

Mathematics

Biology

Operating system

Machine learning

Recurrent neural network

Memristor

Time series

Engineering

Series (stratigraphy)

Paleontology

Artificial intelligence

Range (aeronautics)

Chaotic

Electronic engineering

Geometry

Computer science

Process (computing)

Artificial neural network

Nature Electronics

Time-series analysis including forecasting is essential in a range of fields from finance to engineering. However, long-term forecasting is difficult, particularly for cases where the underlying models and parameters are complex and unknown. Neural networks 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 to time-series analysis and forecasting tasks. Here, we report a reservoir computing hardware system based on dynamic tungsten oxide (WOx) memristors that can efficiently process temporal data. The internal short-term memory effects of the WOx memristors allow the memristor-based reservoir to nonlinearly map temporal inputs into reservoir states, where the projected features can be readily processed by a linear readout function. We use the system to experimentally demonstrate two standard benchmarking tasks: isolated spoken-digit recognition with partial inputs, and chaotic system forecasting. A high classification accuracy of 99.2% is obtained for spoken-digit recognition, and autonomous chaotic time-series forecasting has been demonstrated over the long term. A reservoir computer system based on dynamic tungsten oxide memristors can be used to perform time-series analysis, demonstrating isolated spoken-digit recognition with partial inputs and chaotic system forecasting.

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