Abstract:

The support vector machine is used in lower limb rehabilitation training for device driving system modeling, one of the important issues is to select training parameters. In order to solve this issue, using the simulink platform collected samples, and through the libsvm toolbox, the related interface is experimented. In contrast experiments, three kinds of regression of advanced objective function optimization algorithm are selected. Algorithm in modeling application scope and value is discussed, and the basis for selecting is given. The grid search and particle swarm optimization have a mean square error close to zero, the correlation coefficient is greater than 99%. It applies to the demand of stability and practicality of the system. The genetic algorithm is not stable, but also has practical application. The results show that the method effectively solves the problem of selecting parameters, and at the same time for the further study of the object oriented objective function external optimization algorithm improvements
, especially for high requirements of real-time dynamic identification condition}, provide some basic support.

Key words: system modeling, rehabilitation training device, support vector machine, training parameters