In this paper, it is proposed a methodology of automatic PD classification in hydro-generator stator windings using ... ANN, and its weights and biases are updated iteratively in this work by means of ...

It is a gradient dynamical system, using the energy ... tuning in improving classification accuracy, particularly in optimizing SVM kernel functions and ANN architectures. This study aligns with our ...

In this study, several machine learning algorithms ... as miss-classification rates and average squared error) and iteration plots are constructed on both the entire training data set and the ...

The software supports CNN, DNN and KNN algorithms. The use of CNN and DNN are currently mainstream in the development of deep learning (DL) for ADC classification in the semiconductor industry. We ...

Note that when there are three or more possible values to predict (for example, predict political leaning), the problem is called multi-class classification, which typically uses different algorithms ...

Note that when there are just two possible values to predict (for example, sex = male or female), the problem is called binary classification, which typically uses different algorithms ... trains a ...

1) This article aims to develop the ANN approach for CHB MLI fault diagnosis that utilizes multilayer perceptron (MLP) to detect open circuit faults ... faults and trained using the fault detection ...

therefore these components when connected to a DC microgrid needs more attention and protection against a different type of circuit faults like short ... This paper presents a new ANN-enabled bat ...