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.

Key words: ethylene plant, production capacity forecast, fuzzy C-means cluster, radial basis function neural network, model-predictive control, neural networks, production

摘要：

针对传统的径向基函数(RBF)神经网络隐藏层节点的不确定和初始中心敏感性、收敛速度过慢等问题,提出一种基于模糊C均值的RBF神经网络(FCM-RBF)模型,通过模糊C均值聚类(FCM)得到各聚类中心,基于误差反传的梯度下降法训练隐藏层到输出层之间的权值,克服传统RBF模型对数据中心的敏感性,优化确定RBF神经网络隐藏层的节点数,提高网络训练速度和精度。最后将其用于乙烯装置生产能力预测中,分析预测不同技术、不同规模乙烯装置生产情况,指导乙烯生产,提高生产效率,结果验证了所提出算法的有效性和实用性。

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