化工学报2016，Vol.67Issue(3)：812-819,8.DOI:10.11949/j.issn.0438-1157.20151910
基于模糊RBF神经网络的乙烯装置生产能力预测
Ethylene plants production capacity forecast based on fuzzy RBF neural network
摘要
Abstract
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.关键词
乙烯装置/生产能力预测/模糊C均值聚类/径向基神经网络/模型预测控制/神经网络/生产Key words
ethylene plant/production capacity forecast/fuzzy C-means cluster/radial basis function neural network/model-predictive control/neural networks/production分类
信息技术与安全科学引用本文复制引用
耿志强,陈杰,韩永明..基于模糊RBF神经网络的乙烯装置生产能力预测[J].化工学报,2016,67(3):812-819,8.基金项目
国家自然科学基金项目（61374166,71572008）；高等学校博士学科点专项科研基金（20120010110010）；中央高校基本科研业务费（YS1404，ZY1502）。@@@@supported by the National Natural Science Foundation of China (61374166,71572008), the Specialized Research Fund for the Doctoral Program of Higher Education of China (20120010110010), and the Fundamental Research Funds for the Central Universities(YS1404, ZY1502) （61374166,71572008）
