We report a generalized nonlinear Schr\"odinger equation simulation model of an extreme learning machine based on optical fiber propagation. Using handwritten digit classification as a benchmark, we study how accuracy depends on propagation dynamics, as well as parameters governing spectral encoding, readout, and noise. Test accuracies of over 91% and 93% are found for propagation in the anomalous and normal dispersion regimes respectively. Our simulation results also suggest that quantum noise on the input pulses introduces an intrinsic penalty to ELM performance.

翻译：我们报道了一种基于光纤传播的极限学习机的广义非线性薛定谔方程仿真模型。以手写数字分类为基准，我们研究了分类精度如何依赖于传播动力学，以及控制光谱编码、读出和噪声的参数。分别在反常色散和正常色散传播区域中获得了超过91%和93%的测试准确率。我们的仿真结果还表明，输入脉冲上的量子噪声会对极限学习机的性能产生固有惩罚。