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Reservoir computing with random and optimized time-shifts
Enrico Del Frate1, Afroza Shirin1, Francesco Sorrentino1
1Mechanical Engineering Department, University of New Mexico, Albuquerque, New Mexico 87131, USA.
Chaos (Woodbury, N.Y.)
|January 1, 2022
Summary
Random time-shifts significantly enhance reservoir computer accuracy and performance. A new optimization technique further improves these results, validated through numerical experiments.
Area of Science:
- Computational neuroscience
- Machine learning
Background:
- Reservoir computing is a powerful framework for processing time-series data.
- Optimizing reservoir computer performance is crucial for various applications.
Purpose of the Study:
- To investigate the impact of random time-shifts on reservoir computer accuracy and performance.
- To develop and test an effective method for optimizing these time-shifts.
Main Methods:
- Applying random time-shifts to reservoir computer readouts.
- Evaluating training and testing errors across different reservoir parameters and tasks.
- Developing and implementing an optimization technique for time-shifts.
Main Results:
- Substantial improvements in both accuracy (training error) and performance (testing error) were observed.
- The proposed time-shift optimization technique proved effective in numerical experiments.
Conclusions:
- Random time-shifts are a valuable method for enhancing reservoir computer capabilities.
- The developed optimization technique offers a practical approach to maximizing these benefits.
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