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.

神经网络预测和数据处理在化工行业得到了广泛的应用，但是存在很多影响系统输出的扰动变量，传统的神经网络预测模型精度较差。在本文中，影响输出的归一化输入变量的降维首先通过使用主成分分析（PCA）来实现。然后，建立径向基函数（RBF）神经网络模型。采用Levenberg-Marquardt(LM)算法初始化RBF神经网络的权重，克服了训练过程中初始权重的影响。进一步引入遗传算法（GA）来训练中心、宽度和权重，以提高建模精度。最后，使用均方根误差 (RMSE) 来评估预测性能。