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On using reservoir computing for sensing applications: exploring environment-sensitive memristor networks

机译:关于使用储层计算进行传感应用:探索对环境敏感的忆阻器网络

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Recently, the SWEET sensing setup has been proposed as a way of exploiting reservoir computing for sensing. The setup features three components: an input signal (the drive), the environment and a reservoir, where the reservoir and the environment are treated as one dynamical system, a super-reservoir. Due to the reservoir-environment interaction, the information about the environment is encoded in the state of the reservoir. This information can be inferred (decoded) by analysing the reservoir state. The decoding is done by using an external drive signal. This signal is optimised to achieve a separation in the space of the reservoir states: Under different environmental conditions, the reservoir should visit distinct regions of the configuration space. We examined this approach theoretically by using an environment-sensitive memristor as a reservoir, where the memristance is the state variable. The goal has been to identify a suitable drive that can achieve the phase space separation, which was formulated as an optimization problem, and solved by a genetic optimization algorithm developed in this study. For simplicity reasons, only two environmental conditions were considered (describing a static and a varying environment). A suitable drive signal has been identified based on intuitive analysis of the memristor dynamics, and by solving the optimization problem. Under both drives the memristance is driven to two different regions of the one-dimensional state space under the influence of the two environmental conditions, which can be used to infer about the environment. The separation occurs if there is a synchronisation between the drive and the environmental signals. To quantify the magnitude of the separation, we introduced a quality of sensing index: The ability to sense depends critically on the synchronisation between the drive and environmental conditions. If this synchronisation is not maintained the quality of sensing deteriorates.
机译:近来,已经提出了SWEET感测设置,作为利用储层计算进行感测的一种方式。该设置具有三个组成部分:输入信号(驱动器),环境和水库,其中水库和环境被视为一个动力系统,即超级水库。由于储层与环境的相互作用,有关环境的信息被编码为储层状态。可以通过分析储层状态来推断(解码)此信息。通过使用外部驱动信号完成解码。优化该信号以实现储层状态空间的分离:在不同的环境条件下,储层应访问构造空间的不同区域。我们在理论上通过使用对环境敏感的忆阻器作为存储池(其中忆阻是状态变量)来检查此方法。目的是确定一种合适的驱动器,该驱动器可以实现相空间分离,该驱动器被表述为优化问题,并通过本研究中开发的遗传优化算法来解决。为简单起见,仅考虑两个环境条件(描述静态和变化的环境)。基于对忆阻器动力学的直观分析并通过解决优化问题,已经确定了合适的驱动信号。在这两种驱动下,忆阻都在两个环境条件的影响下被驱动到一维状态空间的两个不同区域,这可以用来推断环境。如果驱动器和环境信号之间存在同步,则会发生分离。为了量化分离的程度,我们引入了一种感应指数质量:感应能力关键取决于驱动器和环境条件之间的同步。如果不保持这种同步,则感测的质量会下降。

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