LSSVM Network Flow Prediction Based on the Self-adaptive Genetic Algorithm Optimization

In order to change the insufficiency of traditional network flow prediction and improve its accuracy, the paper proposed a kind of network flow prediction method based on the self-adaptive genetic least square support vector machine optimization. Through analyzing the individual parameter of the LS-SVM principle and self-adaptive remains algorithm, the network flow prediction model structure of GA-LSSVM, and the genetic model global operation parameters, this paper would conduct a performance test to the network flow simulation experiment. The simulation result showed that: compared with the traditional forecasting methods, the accuracy of its network flow prediction was higher than the traditional forecasting methods by using the least square support vector machine genetic optimization.