论文标题
微林作为储层计算节点：内存/非线性任务和输入非理想的效果
A Microring as a Reservoir Computing Node: Memory/Nonlinear Tasks and Effect of Input Non-ideality
论文作者
论文摘要
光学微孔子的非线性响应用于时间多路复用的储层计算神经网络。在虚拟节点方法中，通过脊回归的离线训练，我们解决了线性和非线性逻辑操作。我们分析了微孔子的非线性作为位和/或神经激活函数之间的记忆。通过控制受逻辑操作的位和脊回归量的位数量之间的距离之间的两个距离来实现这一目标。我们表明，光学微孔子在线性任务中最多显示两个内存的位置，并且允许求解提供内存和非线性的非线性任务。最后，我们证明虚拟节点方法始终需要将储层的性能与通过在输入信号上应用相同的训练过程获得的结果进行比较。
The nonlinear response of an optical microresonator is used in a time multiplexed reservoir computing neural network. Within a virtual node approach combined with an offline training through ridge regression, we solved linear and nonlinear logic operations. We analyzed the nonlinearity of the microresonator as a memory between bits and/or as a neural activation function. This is made possible by controlling both the distance between bits subject to the logical operation and the number of bits supplied to the ridge regression. We show that the optical microresonator exhibits up to two bits of memory in linear tasks and that it allows solving nonlinear tasks providing both memory and nonlinearity. Finally, we demonstrate that the virtual node approach always requires a comparison of the reservoir's performance with the results obtained by applying the same training process on the input signal.
