Abstract
There is a growing demand for physical reservoirs that operate with low power consumption and low computational cost. We have conducted research on the basic properties of Ag2S reservoirs, which are a type of physical reservoir. However, little research has been conducted on their applications. In this study, as a first step toward the practical application of Ag2S reservoirs, we implemented two types of rock-paper-scissors judgment systems using Ag2S reservoirs. In these experiments, we were able to demonstrate fast learning in the reservoir by comparing the results with methods using a single-layer perceptron and a convolutional neural network. In addition, we could obtain a maximum accuracy rate of about 98%.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
