Research on networked synchronization control model by the combination of genetic algorithm and support vector machine

Networked synchronization control system can control the behavior of multi-devices or multi-systems synchronously to realize their synchronous work. Support vector machine is a new learning method, which has considerable applications. However, the parameters of support vector machine have a great influence on its control ability, so in order to gain support vector machine with good control ability, these parameters need to be determined. In the study, genetic algorithm is applied to determine the parameters of support vector machine. Thus, the combination method of support vector machine and genetic algorithm is applied to the networked synchronization control. We employ tracking curve of sine to testify the synchronization control performance of the combination method of genetic algorithm and support vector machine. PID controller is used to compare with the proposed genetic algorithm and support vector machine controller. It is indicated that the networked synchronization control result by GA-SVM controller is better than that by PID controller.