Optimizing the RBF neural network model for an industrial objective by the genetic algorithms
JIN Rong CAO Liu lin (Department of Chemical Automation, Beijing University of Chemical Techology,Beijing 100029,China)
Aimed at the modeling of a typical object in the petrochemical industry, the genetic algorithms,is introduced to design the architecture of RBF neural network, in which the technique of variable length encoding with natural numbers is involved and Akaike's information criterion is chosen as the optimal objective. The fundamentals,concrete procedures of GA and generalization performance tests are presented. The RBF neural network derived by GA has relatively simple configuration and improved generalization performance compared with that derive by the orthogonal least square leaming algorithm.