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Multi-wavelength optical information processing with deep reinforcement learning 被引量:1
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作者 Qiuquan Yan Hao Ouyang +6 位作者 Zilong Tao Meili Shen Shiyin Du Jun Zhang Hengzhu Liu Hao Hao Tian Jiang 《Light(Science & Applications)》 2025年第6期1643-1654,共12页
Multi-wavelength optical information processing systems are commonly utilized in optical neural networks and broadband signal processing.However,their effectiveness is often compromised by frequency-selective response... Multi-wavelength optical information processing systems are commonly utilized in optical neural networks and broadband signal processing.However,their effectiveness is often compromised by frequency-selective responses caused by fabrication,transmission,and environmental factors.To mitigate these issues,this study introduces a deep reinforcement learning calibration(DRC)method inspired by the deep deterministic policy gradient training strategy.This method continuously and autonomously learns from the system,effectively accumulating experiential knowledge for calibration strategies and demonstrating superior adaptability compared to traditional methods.In systems based on dispersion compensating fiber,micro-ring resonator array,and Mach-Zehnder interferometer array that use multiwavelength optical carriers as the light source,the DRC method enables the completion of the corresponding signal processing functions within 21 iterations.This method provides efficient and accurate control,making it suitable for applications such as optical convolution computation acceleration,microwave photonic signal processing,and optical network routing. 展开更多
关键词 calibration deep deterministic policy gradient optical neural networks deep reinforcement learning deep deterministic policy gradient training strategythis multi wavelength optical information processing broadband signal processinghowevertheir
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