The Prediction of Grounding Grid Corrosion Rate Using Optimized RBF Network

Abstract

Because the grounding grid corrosion rate has the property of nonlinearity and uncertainty, it is very difficult for us to predict precisely. The approach is proposed that ant colony clustering algorithm is combined with RBF neural network to predict the grounding grid corrosion rate, using ant colony clustering algorithm to get the center of hidden layer neurons. To find the best clustering result, local search is applied in ant colony algorithm. This model has good performance of strong local generalization abilities and satisfying accuracy. At last, it is proved with lots of experiments that the application is fairly effective.