RBF neural network modeling approach using PCA based LM–GA optimization for coke furnace system

Abstract

Neural network prediction and data processing have been widely used in chemical industry, however, there exist many disturbance variables that will affect the system output, and traditional neural network prediction model has poor accuracy. In this paper, the dimensionality reduction of normalized input variables that affect the outputs is first implemented by using principal component analysis (PCA). Then, radial basis function (RBF) neural network model is established. Levenberg–Marquardt (LM) algorithm is used to initialize the weights of RBF neural network, which overcomes the influence of initial weights during the training process. Genetic algorithm (GA) is further introduced to train the centers, widths and weights to improve the modeling accuracy. Finally, the root mean square error (RMSE) is used to evaluate the prediction performances. Compared with two RBF neural network modeling methods, the proposed method can improve the prediction accuracy greatly.