Prediction of Corrosion Rate of 3C Steel in Sea Water Environment Based on PSO-RBFNN

In order to establish an effective model for prediction of corrosion rate of 3C steel in seawater environment, a radial basis function neural network (RBFNN) prediction model based on particle swarm optimization (PSO) was proposed. The PSO algorithm can automatically tune the centers and spreads of each radial basis function and the connection weights. Meanwhile, the number of radial basis functions of the constructed RBFNN can be automatically minimized by choosing a special fitness function. Therefore, the proposed PSO-RBFNN method can construct the prediction model adaptively with relatively high precision within a short training time. Simulation results demonstrate the proposed model has good prediction accuracy and self-learning ability.