多波长光学信息处理与深度强化学习
Qiuquan Yan1, Hao Ouyang2, Zilong Tao1
1College of Computer Science and Technology, National University of Defense Technology, Changsha, China.
Light, science & applications
|April 14, 2025
概括
一种新的深度强化学习校准 (DRC) 方法通过自主学习校准策略来增强多波长光学系统. 这种方法提高了光学神经网络和信号处理应用中的适应性和准确性.
科学领域:
- 光电学是指光电子产品.
- 机器学习 机器学习
- 光学信号处理 视觉信号处理
背景情况:
- 多波长光学信息处理对于光学神经网络和宽带信号处理至关重要.
- 由制造,传输和环境因素引起的频率选择性响应会降低系统性能.
研究的目的:
- 引入一种新的深度强化学习校准 (DRC) 方法,以减轻多波长光学系统中的频率选择性响应.
- 证明DRC方法能够自主学习和调整校准策略的能力.
主要方法:
- 该研究采用深度强化学习校准 (DRC) 方法,从深度决定性政策梯度培训策略中汲取灵感.
- 通过DRC方法,从系统中进行持续的,自主学习,以积累校准知识.
- 该方法在使用分散补偿纤维,微环共振器阵列和带有多波长光学载波器的马赫-泽恩德干扰仪阵列的系统上进行了测试.
主要成果:
- 在经过测试的系统中,DRC方法在21次代内成功完成了信号处理功能.
- 与传统的校准方法相比,这种方法显示出更高的适应性和学习能力.
- 该系统实现了复杂光学任务的高效准确控制.
结论:
- 深度强化学习校准 (DRC) 方法为多波长光学系统的频率选择性响应提供了有效的解决方案.
- 这种自主学习方法为先进的光学应用提供了强大的和可适应的校准.
- DRC方法适用于加速光学卷积,微波光子信号处理和光学网络路由.
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