跟踪周期为M的离散时间序列

G. Noone, Kaidi Hui, S. Howard
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引用次数: 0

摘要

在信号处理和通信领域中,许多时间序列的周期为M,本质上是离散的和有噪声的。我们不仅经常想要准确地估计这样一个数字信号的M个参数,我们还需要学习参数的“发射序列”,以便我们能够及时预测下一个事件。这是通过结合两个神经网络来实现的。第一个网络对连续事件之间的时间差进行聚类,以准确估计参数,第二个网络学习预测序列中的下一个参数。因此,我们能够有效地跟踪周期为M的离散时间序列。这种神经方法,一般来说,只需要几个完整的帧或周期的时间序列,以便收敛,即使是复杂的序列。
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Tracking period M discrete time series
In the field of signal processing and communications, many time series are period M, discrete and noisy in nature. Not only do we often want to accurately estimate the M parameters of such a digital signal, we also require to learn the "firing sequence" of the parameters so that we can predict the next event in time. This is achieved by combining two neural nets. The first net clusters on the time difference between successive events to accurately estimate the parameters and the second net learns to predict which parameter is to be next in the sequence. Hence we are effectively able to track a period M discrete time series. This neural method, in general, requires only a few complete frames or cycles of the time series in order to converge, even for complicated sequences.
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