The Gradual Optimization of Grounding Grip Corrosion Rate Forecasting Model

Abstract

A forecasting model of the gradual optimization algorithm is established to predict substation grounding grip corrosion rate. In this model, according to the “Over Fitting” phenomenon in the neural network limited soil corrosion sample data are randomly combined and the training stops when the training error and validation error are equal. The model of smaller errors will be chosen as the optimal model. As shown in the simulation, the general performance and fitting accuracy from the forecasting model meet requirements.