Exploration of Static and Time Dependent Neural Network Techniques for the Prediction of Ice Accretion on Overhead Line Conductors
نویسندگان
چکیده
In order to predict the ice accretion on overhead line conductors, five artificial neural network (ANN) architectures were explored and compared. Two static networks, Multilayer Perceptron and Radial Basis Functions, as well as two time dependent networks, Finite Impulse Response and Elman, were compared with multiple linear regression (ADALINE). Results indicated that the FIR network yielded the best prediction and would be a good candidate as a predictor. The static networks yielded poor results and were ill-adapted for the task.
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