The increasing popularity of machine learning solutions puts increasing restrictions on this field if it is to penetrate more aspects of life. In particular, energy efficiency and speed of operation is crucial, inter alia in portable medical devices. The Reservoir Computing (RC) paradigm poses as a solution to these issues through foundation of its operation: the reservoir of states. Adequate separation of input information translated into the internal state of the reservoir, whose connections do not need to be trained, allow to simplify the readout layer thus significantly accelerating the operation of the system. In this brief review article, the theoretical basis of RC was first described, followed by a description of its individual variants, their development and state-of-the-art applications in chemical sensing and metrology: detection of impedance changes and ion sensing. Presented results indicate applicability of reservoir computing for sensing and validating the SWEET algorithm experimentally.

中文翻译：

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用于传感的储层计算：一种实验方法

如果机器学习解决方案要渗透到生活的更多方面，那么机器学习解决方案的日益普及对该领域施加了越来越多的限制。尤其是在便携式医疗设备中，能源效率和操作速度尤其重要。水库计算 (RC) 范式通过其操作的基础：状态的水库，提出了这些问题的解决方案。输入信息的充分分离转化为不需要训练其连接的储层的内部状态，可以简化读出层，从而显着加速系统的运行。在这篇简短的评论文章中，首先描述了 RC 的理论基础，然后描述了其各个变体、它们的发展以及在化学传感和计量学中的最新应用：检测阻抗变化和离子传感。所呈现的结果表明储层计算在实验上检测和验证 SWEET 算法的适用性。