Abstract
Reservoir computing is highly compatible with physical waves such as optical wave and spin wave. In such wave-realized reservoir computing, signals are not limited to the raw signals, or baseband signals, but also expressed as frequency-shifted signals having a carrier. This paper proposes such a reservoir computing architecture, namely, carrier-wave reservoir computing. We present its construction and suitable learning dynamics with which we deal with the phase information explicitly. The merits of the proposed carrier-wave reservoir computing are its frequency-dependent processing functions, frequency-domain multiplexing ability and explicit phase-information utilization. It is also useful for material evaluation from the viewpoint of computational ability in the reservoir computing.
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Hirose, A. et al. (2018). Proposal of Carrier-Wave Reservoir Computing. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_56
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