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Reservoir computing with noise
Chad Nathe1, Chandra Pappu2, Nicholas A Mecholsky3
1Mechanical Engineering Department, University of New Mexico, Albuquerque, New Mexico 87131, USA.
Chaos (Woodbury, N.Y.)
|April 25, 2023
Summary
Measurement noise impacts reservoir computing performance. Optimal performance occurs when training and testing noise levels match, and low-pass filtering can mitigate noise effects.
Area of Science:
- Computational neuroscience
- Nonlinear dynamics
Background:
- Reservoir computing (RC) is a powerful machine learning paradigm for time-series processing.
- Chaotic systems present complex dynamics that are challenging to model and predict.
- Measurement noise is an inherent challenge in real-world applications of RC.
Purpose of the Study:
- To investigate the detailed effects of measurement noise on reservoir computing performance.
- To analyze how noise impacts different phases of reservoir computing, specifically training and testing.
- To identify strategies for mitigating the adverse effects of noise in RC applications.
Main Methods:
- Simulations of a chaotic system using a reservoir computer model.
- Systematic variation of measurement noise levels during training and testing phases.
- Application of low-pass filtering techniques to input and signal data.
Main Results:
- Reservoir computing performance is sensitive to the level of measurement noise.
- The optimal performance is achieved when the noise intensity is consistent between the training and testing phases.
- Low-pass filtering of input and signal data effectively preserves reservoir performance while reducing noise.
Conclusions:
- Measurement noise significantly influences reservoir computing accuracy, particularly in chaotic system modeling.
- Matching noise levels during training and testing is crucial for robust reservoir computing performance.
- Low-pass filtering is a viable and effective method to enhance the resilience of reservoir computing to noise.
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