Emergence of a stochastic resonance in machine learning

• URL: http://arxiv.org/abs/2211.09955v1
• Date: Tue, 15 Nov 2022 18:15:43 GMT
• Title: Emergence of a stochastic resonance in machine learning
• Authors: Zheng-Meng Zhai, Ling-Wei Kong, and Ying-Cheng Lai
• Abstract summary: We find that injecting noise to the training data can induce a resonance with significant benefits to both short-term prediction of the state variables and long-term prediction of the attractor of the system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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