Emergence of a stochastic resonance in machine learning
This addresses the challenge of improving machine learning predictions for chaotic systems, offering a novel approach that could benefit fields like weather forecasting or physics simulations, though it appears incremental as it builds on existing reservoir computing methods.
The study tackled the problem of predicting chaotic systems by showing that injecting noise into training data can induce stochastic resonance, which significantly improves prediction accuracy, stability, and horizon in reservoir computers, as demonstrated on two high-dimensional chaotic systems.
Can noise be beneficial to machine-learning prediction of chaotic systems? Utilizing reservoir computers as a paradigm, we find that injecting noise to the training data can induce a stochastic resonance with significant benefits to both short-term prediction of the state variables and long-term prediction of the attractor of the system. A key to inducing the stochastic resonance is to include the amplitude of the noise in the set of hyperparameters for optimization. By so doing, the prediction accuracy, stability and horizon can be dramatically improved. The stochastic resonance phenomenon is demonstrated using two prototypical high-dimensional chaotic systems.