On Improving the Computing Capacity of Dynamical Systems

Artikel i vetenskaplig tidskrift, 2020

Reservoir Computing has emerged as a practical approach for solving temporal pattern recognition problems. The procedure of preparing the system for pattern recognition is simple, provided that the dynamical system (reservoir) used for computation is complex enough. However, to achieve a sufficient reservoir complexity, one has to use many interacting elements. We propose a novel method to reduce the number of reservoir elements without reducing the computing capacity of the device. It is shown that if an auxiliary input channel can be engineered, the drive, advantageous correlations between the signal one wishes to analyse and the state of the reservoir can emerge, increasing the intelligence of the system. The method has been illustrated on the problem of electrocardiogram (ECG) signal classification. By using a reservoir with only one element, and an optimised drive, more than 93% of the signals have been correctly labelled.