For the conventional radial basis function (RBF) neural network

there are many problems like uncertain nodes in the hidden layer

sensitivity to initial centers and slow convergence speed

etc. This paper proposes an RBF neural network model based on the fuzzy C-means method (FCM-RBF)

with each cluster center obtained by fuzzy C-means clustering. And weights between the hidden layer and the output layer are trained by the gradient descent method based on error back-propagation (BP). The proposed method overcomes the sensitivity of the data center for traditional RBF model

determines optimally the number of nodes in the hidden layer of RBF neural network

and improves the network training speed and precision. Finally

the proposed method is applied in the production capacity forecast of the ethylene plants. The production statuses of ethylene plants of different technologies or different scales are analyzed and predicted to guide the ethylene production and improve energy efficiency. The empirical results demonstrate the effectiveness and practicability of the proposed algorithm.