Improved RBF neural network with double model structure and its application

CIESC Journal ›› 2011, Vol. 62 ›› Issue (8): 2345-2349.

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Improved RBF neural network with double model structure and its application

LI Quanshan,ZHANG Yishan,CAO Liulin,LIN Xiaolin,CUI Jia

Online:2011-08-05 Published:2011-08-05

改进的双模型结构RBF神经网络及其应用

李全善,张义山,曹柳林,林晓琳,崔佳

北京化工大学信息科学与技术学院,北京100029; 中国石油辽阳石化分公司,辽宁辽阳 111003;北京世纪隆博科技有限责任公司,北京 100020

Abstract

Abstract:

A dual model RBF(radial basis function)neural network was proposed in this paper.One is used for self-learning,which learns one time a day.The other is used for on-line correcting,which is the running model currently.Both the self-learning model and the on-line correcting model are corrected six times every day and should track the current conditions of the system quickly.At the same time,the accuracy of the two models should be compared.If the accuracy of the on-line correcting model is less than the one of the self-learning model,the latter becomes the new currently running model instead of the old one. Otherwise,the currently model is maintained.To solve the problem of neural network large prediction errors,a network algorithm analysis is given and the influence factors of the network prediction accuracy are found.At last,an improved algorithm of RBF neural network modeling is proposed,which combines K-means clustering method with the recursive descent algorithm.Simulation and practical application proved the effectiveness of the improved method.

摘要：

提出了离线结构学习和在线权值校正相结合的双模型结构RBF神经网络，以离线学习和在线校正相结合的方式实现网络的自学习和自校正，满足了软测量仪表现场应用的要求。针对应用过程中出现预测误差过大的现象，通过对网络算法进行分析，研究影响网络预测精度的因素，在此基础上，提出了以K均值聚类法和递推下降算法相结合的RBF神经网络建模改进算法，仿真结果和实际应用证明了改进算法的有效性。

CLC Number:

TP 183

LI Quanshan, ZHANG Yishan, CAO Liulin, LIN Xiaolin, CUI Jia. Improved RBF neural network with double model structure and its application[J]. CIESC Journal, 2011, 62(8): 2345-2349.

李全善, 张义山, 曹柳林, 林晓琳, 崔佳. 改进的双模型结构RBF神经网络及其应用 [J]. 化工学报, 2011, 62(8): 2345-2349.

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Improved RBF neural network with double model structure and its application

改进的双模型结构RBF神经网络及其应用

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