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Dynamic boltzmann machine for predicting general distributions of time series datasets
Dynamic boltzmann machine for predicting general distributions of time series datasets
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机译:动态Boltzmann机器,用于预测时间序列数据集的一般分布
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摘要
A computer-implemented method includes employing a dynamic Boltzmann machine (DyBM) to solve a maximum likelihood of generalized normal distribution (GND) of time-series datasets. The method further includes acquiring the time-series datasets transmitted from a source node to a destination node of a neural network including a plurality of nodes, learning, by the processor, a time-series generative model based on the GND with eligibility traces, and, performing, by the processor, online updating of internal parameters of the GND based on a gradient update to predict updated times-series datasets generated from non-Gaussian distributions.
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