论文标题
基于氧化还原的离子门控库，利用排水管和门非线性响应中的双储液状态
A Redox-based Ion-Gating Reservoir, Utilizing Double Reservoir States in Drain and Gate Nonlinear Responses
论文作者
论文摘要
我们已经通过基于氧化还原的离子门控储液（氧化还原）（包括Lixwo3薄膜和锂离子导电玻璃陶瓷（LICGC））展示了物理储层计算。受试者氧化还原-IGR通过利用LixWO3通道中的电压脉冲驱动离子控制来成功求解了二阶非线性动态方程，以启用储层计算。在正常条件下，在储层状态下仅使用排水电流（ID），最低的预测误差为7.39x10-4。通过向储层状态添加IG来提高性能，从而显着降低了预测误差为5.06x10-4，这明显低于迄今为止报告的其他类型的物理储层。还通过IGR进行了二阶非线性自回旋运动平均（NARMA2）任务，这是储层计算的典型基准，并实现了良好的性能，NMSE为0.163。执行了一项短期记忆任务，以研究IG添加引起的增强机制。在遗忘曲线中观察到内存能力从没有IG的1.87增加到IG的2.73，这表明高维度和记忆能力的增强归因于性能改善的起源。
We have demonstrated physical reservoir computing with a redox-based ion-gating reservoir (redox-IGR) comprising LixWO3 thin film and lithium ion conducting glass ceramic (LICGC). The subject redox-IGR successfully solved a second-order nonlinear dynamic equation by utilizing voltage pulse driven ion-gating in a LixWO3 channel to enable reservoir computing. Under the normal conditions, in which only the drain current (ID) is used for the reservoir states, the lowest prediction error is 7.39x10-4. Performance was enhanced by the addition of IG to the reservoir states, resulting in a significant lowering of the prediction error to 5.06x10-4, which is noticeably lower than other types of physical reservoirs reported to date. A second-order nonlinear autoregressive moving average (NARMA2) task, a typical benchmark of reservoir computing, was also performed with the IGR and good performance was achieved, with an NMSE of 0.163. A short-term memory task was performed to investigate an enhancement mechanism resulting from the IG addition. An increase in memory capacity, from 1.87 without IG to 2.73 with IG, was observed in the forgetting curves, indicating that enhancement of both high dimensionality and memory capacity are attributed to the origin of the performance improvement.
