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Kazutaka Kanno1, Makoto Naruse2, Atsushi Uchida3

  • 1Department of Information and Computer Sciences, Saitama University 255 Shimo-Okubo, Sakura-ku, Saitama City, Saitama, 338-8570, Japan. kkanno@mail.saitama-u.ac.jp.

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Area of Science:

  • * Explores the intersection of photonic computing, artificial intelligence, and machine learning.
  • * Focuses on advanced computing paradigms beyond traditional von Neumann architectures.

Background:

  • * Photonic reservoir computing excels with known data but degrades when environmental characteristics change.
  • * Existing systems lack adaptability to novel or evolving data patterns.

Purpose of the Study:

  • * To develop an adaptive model selection scheme for photonic reservoir computing.
  • * To enable autonomous system performance optimization in dynamic environments.
  • * To enhance the robustness of photonic AI for real-world applications.

Main Methods:

  • * Implemented a reinforcement learning framework for adaptive model selection.
  • * Utilized dynamic source models generating temporal waveforms.
  • * Employed prediction errors as rewards to guide model selection.

Main Results:

  • * Successfully demonstrated adaptive model selection for time series prediction.
  • * Achieved accurate model selection for temporally mixed signals from different models.
  • * Validated performance with signals from the same model but varying parameters.

Conclusions:

  • * The proposed scheme enables autonomous adaptation in photonic reservoir computing.
  • * This advancement is crucial for AI applications in unpredictable environments.
  • * Paves the way for photonic AI in load forecasting and multi-objective control.