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Titlebook: Application of FPGA to Real‐Time Machine Learning; Hardware Reservoir C Piotr Antonik Book 2018 Springer International Publishing AG, part

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楼主: Magnanimous
发表于 2025-3-25 03:20:53 | 显示全部楼层
https://doi.org/10.1007/978-3-031-08533-8ble reservoir computer with an analogue readout layer. We also considered a nonlinear output layer, which would be very difficult to train with traditional methods. We show numerically that online learning allows to circumvent the added complexity of the analogue layer and obtain the same level of performance as with a digital layer.
发表于 2025-3-25 09:13:52 | 显示全部楼层
,Photonic Reservoir Computer with Output Feedback,to generate periodic time series and to emulate chaotic systems. We study in detail the effect of experimental noise on system performance. In the case of chaotic systems, we introduce several metrics, based on standard signal-processing techniques, to evaluate the quality of the emulation.
发表于 2025-3-25 13:45:22 | 显示全部楼层
Towards Online-Trained Analogue Readout Layer,ble reservoir computer with an analogue readout layer. We also considered a nonlinear output layer, which would be very difficult to train with traditional methods. We show numerically that online learning allows to circumvent the added complexity of the analogue layer and obtain the same level of performance as with a digital layer.
发表于 2025-3-25 15:56:16 | 显示全部楼层
Frédéric Basso,Carsten Herrmann-Pillathhree benchmark tasks, decreases considerably. This demonstrates that electro-optical analog computers can embody a large part of their own training process, allowing them to be applied to new, more difficult tasks.
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Online Training of a Photonic Reservoir Computer,in 2015 with an idea of implementing the backpropagation training algorithm in hardware, using our opto-electronic reservoir computer with one slight modification. We found that, compared when the backpropagation algorithm is not used, the error rate of the resulting computing device, evaluated on t
发表于 2025-3-26 08:56:28 | 显示全部楼层
Backpropagation with Photonics,in 2015 with an idea of implementing the backpropagation training algorithm (more on that in Sect. .) in hardware, using our opto-electronic reservoir computer (see Sect. .) with one slight modification.
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