中国化学工程学报（英文版）
中國化學工程學報（英文版）
중국화학공정학보（영문판）
CHINESE JOURNAL OF CHEMICAL ENGINEERING
2007年
2期
240-246
,共7页
RBF network%structure optimization%genetic algonthn%splicing system
A splicing system based genetic algorithm is proposed to optimize dyrnamical radial basis function (RBF) neural network,which is used to extract valuable process information from input output data.The novel RBF network training technique includes the network structure into the set of function centers by compromising between the conflicting requirements of reducing prediction error and simultaneously decreasing model complexity.The effectiveness of the proposed method is illustrated through the development of dynamic models as a benchmark discrete example and a continuous stirred tank reactor by comparing with several different RBF network training methods.
