孤波与生物启发式输入数据非线性变换实现的物理储层计算
Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data
AI总结：
本文利用液膜表面孤波实现物理储层计算，以输入数据的非线性变换替代随机性，并用微控制器验证其可作为传统RC算法下一代改进的简单硬件对应物。
摘要：
储层计算（Reservoir Computing, RC）系统可利用随机连接的人工神经网络的非线性动力学特性，高效预测混沌时间序列。RC系统的多功能性推动了针对传统RC算法硬件对应物及更高效的类RC方案的研究。受活体生物大脑中非线性过程的启发，并利用流动液膜表面激发的孤波，本文实验验证了一个物理RC系统，该系统用输入数据的非线性变换替代了随机性的作用。使用计算能力极小的微控制器执行所有操作，我们证明了所设计的RC系统可作为传统RC算法“下一代”改进的技术上简单的硬件对应物。
英文摘要：
Reservoir computing (RC) systems can efficiently forecast chaotic time series using nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper we experimentally validate a physical RC system that substitutes the effect of randomness for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with a minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the `next-generation' improvement of the traditional RC algorithm.
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