The nonlinear transformation used in reservoir computing can be effectively replaced by nonlinear vector autoregression (NVAR) for data prediction. In such a method, also known as next generation reservoir computing (NGRC), the input signal consists of a linear part, including several previous data points, and their nonlinear combinations. Here we show that the application of this method to a network with memristive weights (memristors) can be used to predict signals, depending on the nature of the nonlinear functions and the number of memristors. The network allows an accurate prediction of chaotic time series of Mackey-Glass and Duffing oscillators

Next Generation Memristor Reservoir Computing / Nikiruy, K.; Ivanov, T.; Ziegler, M.; Rossetti, D.; Corinto, F.; Ascoli, A.; Tetzlaff, R.; Demirkol, A. S.; Schmitt, N.. - (2024), pp. 912-917. (Intervento presentato al convegno 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) tenutosi a St Albans (UK) nel 21-23 October 2024) [10.1109/metroxraine62247.2024.10796786].

Next Generation Memristor Reservoir Computing

Rossetti, D.;Corinto, F.;Ascoli, A.;Tetzlaff, R.;
2024

Abstract

The nonlinear transformation used in reservoir computing can be effectively replaced by nonlinear vector autoregression (NVAR) for data prediction. In such a method, also known as next generation reservoir computing (NGRC), the input signal consists of a linear part, including several previous data points, and their nonlinear combinations. Here we show that the application of this method to a network with memristive weights (memristors) can be used to predict signals, depending on the nature of the nonlinear functions and the number of memristors. The network allows an accurate prediction of chaotic time series of Mackey-Glass and Duffing oscillators
2024
979-8-3503-7800-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2996958