Literature DB >> 34972324

Reservoir computing with random and optimized time-shifts.

Enrico Del Frate1, Afroza Shirin1, Francesco Sorrentino1.   

Abstract

We investigate the effects of application of random time-shifts to the readouts of a reservoir computer in terms of both accuracy (training error) and performance (testing error). For different choices of the reservoir parameters and different "tasks," we observe a substantial improvement in both accuracy and performance. We then develop a simple but effective technique to optimize the choice of the time-shifts, which we successfully test in numerical experiments.

Entities:  

Year:  2021        PMID: 34972324      PMCID: PMC8684442          DOI: 10.1063/5.0068941

Source DB:  PubMed          Journal:  Chaos        ISSN: 1054-1500            Impact factor:   3.642


  25 in total

1.  LSTM recurrent networks learn simple context-free and context-sensitive languages.

Authors:  F A Gers; E Schmidhuber
Journal:  IEEE Trans Neural Netw       Date:  2001

2.  An application of recurrent nets to phone probability estimation.

Authors:  A J Robinson
Journal:  IEEE Trans Neural Netw       Date:  1994

3.  On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD.

Authors:  Erik Bollt
Journal:  Chaos       Date:  2021-01       Impact factor: 3.642

4.  Attractor reconstruction by machine learning.

Authors:  Zhixin Lu; Brian R Hunt; Edward Ott
Journal:  Chaos       Date:  2018-06       Impact factor: 3.642

5.  Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach.

Authors:  Jaideep Pathak; Brian Hunt; Michelle Girvan; Zhixin Lu; Edward Ott
Journal:  Phys Rev Lett       Date:  2018-01-12       Impact factor: 9.161

6.  Stability analysis of reservoir computers dynamics via Lyapunov functions.

Authors:  Afroza Shirin; Isaac S Klickstein; Francesco Sorrentino
Journal:  Chaos       Date:  2019-10       Impact factor: 3.642

7.  Performance boost of time-delay reservoir computing by non-resonant clock cycle.

Authors:  Florian Stelzer; André Röhm; Kathy Lüdge; Serhiy Yanchuk
Journal:  Neural Netw       Date:  2020-01-15

8.  Network structure effects in reservoir computers.

Authors:  T L Carroll; L M Pecora
Journal:  Chaos       Date:  2019-08       Impact factor: 3.642

9.  Parallel photonic information processing at gigabyte per second data rates using transient states.

Authors:  Daniel Brunner; Miguel C Soriano; Claudio R Mirasso; Ingo Fischer
Journal:  Nat Commun       Date:  2013       Impact factor: 14.919

10.  Fully analogue photonic reservoir computer.

Authors:  François Duport; Anteo Smerieri; Akram Akrout; Marc Haelterman; Serge Massar
Journal:  Sci Rep       Date:  2016-03-03       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.