At the same time, more advanced parameter optimization algorithms, such as adaptive learning rate adjustment algorithm and super-parameter optimization method combining random search and grid search, are adopted to reduce the risk of over-fitting and improve the generalization ability of the model....
Full size image For a more rigorous validation of the LSTM-based protective strategy, we extend our tests to ascertain the isolation of only the faulted line. We conducted a comprehensive study after training relevant parameters via our LSTM algorithm. We triggered an internal PTP fault with 40 ...
Maximum likelihood from incomplete data via the em algorithm. journal of the royal statistical society. Series B (Methodological). 1977;39(1):1–38. Available from: http://www.jstor.org/stable/2984875. Google Scholar Batista GEAPA, Monard MC. A Study of K-Nearest Neighbour as an ...
Computational complexity. As with Mozer's focused recurrent backprop algorithm (Mozer 1989), only the derivatives @scj @wil need to be stored and updated. Hence the LSTM algorithm is very ecient, with an excellent update complexity of O(W), where W the number of weights (see details in ap...
The based calculation behind this function is the sum algorithm as: . The more details of relevant model and technique are introduced in the following subsections.3 ESTABLISHMENT OF THE PROPOSED CONV-ELSTM 3.1 Feature fusion with convolutional residual model To address the sensitivity of wind power...
The process of local model training on each node employs a gradient descent algorithm with a learning rate of 0.01. Model updates obtained are then aggregated by a weighted average method in which weights correspond to the size of the local dataset. This process called aggregation rounds are ...
Full size table Donoho first proposed the WT algorithm in 1995 40. The algorithm has excellent discriminative ability, adaptability to time-varying signal processing for non-smooth signals and intense noise suppression ability, especially for mutating signals 41. Pivotal threshold determination and quanti...
our proposed framework introduces a novel hybrid model, IChOA-CNN-LSTM, which leverages Convolutional Neural Networks (CNNs) for precise image feature extraction, Long Short-Term Memory (LSTM) networks for sequential data analysis, and an Improved Chimp Optimization Algorithm for effective feature fusi...
Full size image Furthermore, we employed the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance in the dataset, as outlined in the supplementary material. SMOTE generates synthetic samples for minority classes, promoting a more balanced class distribution. This technique enhances...
dtype=torch.long)# Compute log sum exp in a numerically stable way for the forward algorithmdefl...