We present a method to improve the performance of a reservoir computer by keeping the reservoir fixed and increasing the number of output neurons. The additional neurons are nonlinear functions, typically chosen randomly, of the reservoir neurons. We demonstrate the interest of this expanded output layer on an experimental opto-electronic system subject to slow parameter drift which results in loss of performance. We can partially recover the lost performance by using the output layer expansion. The proposed scheme allows for a trade-off between performance gains and system complexity.

中文翻译：

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用于无监督参数漂移补偿的具有输出扩展的光子储层计算机

我们提出了一种通过保持水库固定并增加输出神经元数量来提高水库计算机性能的方法。额外的神经元是储层神经元的非线性函数，通常是随机选择的。我们证明了这种扩展输出层对实验光电系统的兴趣，该系统受到缓慢参数漂移的影响，从而导致性能损失。我们可以通过使用输出层扩展来部分恢复损失的性能。所提出的方案允许在性能增益和系统复杂性之间进行权衡。