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The Decoder Performance Comparison in the Closed-Loop BMI System Based on MPC

机译:基于MPC的闭环BMI系统中解码器性能比较

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In recent years, some emerging technologies, such as back propagation (BP) neural network and recurrent neural networks (RNN) with long-short term memory (LSTM), have been adopted in many fields. Hence, we firstly design four decoders based on the classical Wiener filter and Kalman filter, and the emerging BP neural network and RNN with LSTM, and compare the performance of these decoders offline. Secondly, considering the advantages of the model predictive control (MPC), we design an auxiliary controller based on MPC algorithm to form a closed-loop BMI system, and compare the performance of four decoders online.
机译:近年来,一些新兴技术,例如反向传播(BP)神经网络和具有长期短期记忆(LSTM)的递归神经网络(RNN),已在许多领域中得到采用。因此,我们首先基于经典的Wiener滤波器和Kalman滤波器,以及新兴的BP神经网络和具有LSTM的RNN设计四个解码器,然后离线比较这些解码器的性能。其次,考虑到模型预测控制(MPC)的优势,我们设计了一种基于MPC算法的辅助控制器,构成了闭环BMI系统,并在线比较了四个解码器的性能。

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