and enhance the precision in LSTM predicting; in comparison with alternative models, the ESMD-EWT-SVD-LSTM coupled model shows the highest accuracy in predicting results, with MAE of 4.96, RMSE of 6.13, and SI of 0.12, indicating that the ESMD-EWT-SVD-LSTM model has strong nonlinear process...
复杂的 LSTM 受 saddle point structures 带来的各种问题更严重，而基于 SVD/QR 的 orthogonal initializa...
因为 orthogonal matrix 的所有 vectors 都是 orthonormal 的，也就是不仅 orthogonal，还 magnitude 为 1. 这样，在计算时候，乘上这个 matrix，就可以修正 vanishing 也可以重置 saturation。（2）这个问题应该是和 saddle point 有关系，复杂的 LSTM 受 saddle point structures 带来的各种问题更严重，而...
复杂的 LSTM 受saddle point structures带来的各种问题更严重，而基于 SVD/QR的 orthogonal initialization ...
《为什么 LSTM 在参数初始化时要使用 SVD 方法使参数正交? - 知乎》 http://t.cn/RaUjl6k
It is proves that the prediction result of SVD-LSTM is better by the comparison with traditional prediction model results. The model improves the prediction accuracy. The analysis of a university in Wuhan shows that the prediction effect and accuracy of the proposed predictio...
LSTM.This study recommends a new time series forecasting model, namely ICEEMDAN - SVD - LSTM model, which coalesces Improved Complete Ensemble EMD with Adaptive Noise, Singular Value Decomposition and Long Short Term Memory network. It can be applied to analyse Non-linear and non-stationary data...
First, the improved wavelet threshold denoising (WTD) combined with the optimized singular value decomposition ( SVD) method is used to denoise the sea clutter. Then, the improved whale optimization algorithm (WOA) is used to optimize the hyperparameters of long...
高校负荷、空调负荷特性、奇异值分解、长短期记忆网络、负荷预测、相关性系数准确预测高校空调负荷是保证高校安全用电和电力高峰期区域配电网稳定运行的前提.文中以高校空调负荷中具有代表性的学生宿舍空调负荷为对象,建立了基于奇异值分解-长短期记忆网络的高校学生宿舍空调负荷预测模型.该方法以高校学生宿舍空调负荷特性为...
saddle point 有关系，复杂的 LSTM 受saddle point structures带来的各种问题更严重，而基于 SVD/QR的 ...