融合最优控制与生物可实现学习，降低物理神经网络训练成本并抗噪声。
Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks
- 结合最优控制与直接反馈对齐方法，高效训练连续时间物理神经网络。
- 在光电子延迟系统中验证，训练时间显著缩短且对测量噪声鲁棒。
- 无需系统细节信息，适合硬件实现，拓展了物理神经网络应用范围。
人工智能日益增长的计算需求推动了超越传统数字计算机的计算范式探索。物理神经网络（PNNs）通过利用物理过程的固有计算能力，实现了高效的类脑信息处理；然而，其权重参数的训练成本高昂。本文提出一种训练方法，显著降低该训练成本。该方法将针对连续时间动力系统的设计最优控制方法与一种生物可实现的训练机制——直接反馈对齐（direct feedback alignment）相结合。除大幅减少训练时间外，该方法在存在测量误差和噪声的情况下仍能实现稳健处理，且无需依赖系统的详细信息。该方法在光电子延迟系统中通过数值模拟和实验验证了有效性。本工作显著扩展了可实际用于构建物理神经网络的物理系统范围。
原文摘要 · Abstract (English)
The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical neural networks (PNNs) offer efficient neuromorphic information processing by harnessing the innate computational power of physical processes; however, training their weight parameters is computationally expensive. We propose a training approach for substantially reducing this training cost. Our training approach merges an optimal control method for continuous-time dynamical systems with a biologically plausible training method--direct feedback alignment. In addition to the reduction of training time, this approach achieves robust processing even under measurement errors and noise without requiring detailed system information. The effectiveness was numerically and experimentally verified in an optoelectronic delay system. Our approach significantly extends the range of physical systems practically usable as PNNs.
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