Sebastian Alveteg, Marc Sciamanna, Alex Fuerbach, Delphine Wolfersberger
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引用次数: 0
Abstract
Reservoir computing (RC) is a machine learning (ML) framework that has gained attention in recent years as the interest in alternative computing paradigms has grown. RC allows the utilization of physical systems to solve ML tasks. We demonstrate the use of the nonlinear photorefractive reservoir computer and perform tasks requiring both nonlinearity and memory, such as chaotic time series prediction. Changing the photorefractive response by adjusting the applied field and laser power controls the characteristics of the reservoir. Optimizing the characteristics of the reservoir for performing a 10-step Mackey-Glass (MG) time series prediction, we achieve a mean square error (MSE) of 5x10-4.
期刊介绍:
The Optical Society (OSA) publishes high-quality, peer-reviewed articles in its portfolio of journals, which serve the full breadth of the optics and photonics community.
Optics Letters offers rapid dissemination of new results in all areas of optics with short, original, peer-reviewed communications. Optics Letters covers the latest research in optical science, including optical measurements, optical components and devices, atmospheric optics, biomedical optics, Fourier optics, integrated optics, optical processing, optoelectronics, lasers, nonlinear optics, optical storage and holography, optical coherence, polarization, quantum electronics, ultrafast optical phenomena, photonic crystals, and fiber optics. Criteria used in determining acceptability of contributions include newsworthiness to a substantial part of the optics community and the effect of rapid publication on the research of others. This journal, published twice each month, is where readers look for the latest discoveries in optics.