Identification of Nonlinear System via Svr Optimized by Particle Swarm Algorithm
نویسندگان
چکیده
Given the influence of the selection of regression parameters on the accuracy of SVR model and its ability of learning and generalization, this article adopts the particle swarm optimization algorithm to build the SVR model and applies it to the modeling of nonlinear system identification. Through the simulation experiments, it is found that this model is more accurate in identification and has a stronger ability of learning and generalization compared with GA. In addition, it demonstrates that the application in nonlinear system identification based on PSO-SVR algorithm could be considerably effective.
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