使用互信息熵方法分析了风电功率的气象数据集,剔除关联性小的变量,对气象数据集进行降维处理.并采用嵌入(embedding)结构学习风电功率时间分类特征.随后将TCNN处理后的时序数据,降维后的气象数据以及时间分类特征数据一起送入BNN-LSTM预测模型,通过在某风电数据集不同算法的概率预测指标pinball损失和Winkler评分的对比验证,...
Hi all, I've spent some time experimenting with the BNNS (Accelerate) LSTM-related APIs lately and despite a distinct lack of documentation (even though the headers have quite a few) a got most things to a point where IthinkI know what's going on and I get the expected results. Howeve...
The number of elements in the input. iOS 14.0–18.0DeprecatediPadOS 14.0–18.0DeprecatedMac Catalyst 14.0–18.0DeprecatedmacOS 11.0–15.0DeprecatedtvOS 14.0–18.0DeprecatedvisionOS 1.0–2.0DeprecatedwatchOS 7.0–11.0Deprecated var input_size: Int
var seq_len: Int Discussion If you set this value to a nonzero value that doesn’t match the corresponding array descriptor’s size, BNNS throws an error.Current page is seq_len Apple Developer Documentation Platforms iOS iPadOS macOS tvOS visionOS watchOS Tools Swift SwiftUI Swift Playg...
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Compared to the SARIMA-GARCH model, the proposed LSTM-BNN model outperforms in predicting both the mean and interval of the traffic flow. Especially, the experiments show that the LSTM-BNN model is superior during the daytime and under non-seasonal traffic conditions. The proposed LSTM-BNN ...