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Quantum Neural Network-Based EEG Filtering for a Brain–Computer Interface

机译:基于量子神经网络的脑电接口脑电滤波

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摘要

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics.
机译:本文提出了一种受量子力学启发并结合著名的薛定inger波动方程的新型神经信息处理架构。所提出的称为递归量子神经网络(RQNN)的体系结构可以将非平稳随机信号表征为时变波包。强大的无监督学习算法使RQNN可以有效地捕获输入信号的统计行为,并有助于估计具有未知特性的噪声中嵌入的信号。

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