Self-learning controller using support vector machines and fuzzy inference system
支持向量机-模糊推理自学习控制器设计

As conventional fuzzy inference system (FIS) was derived from expert experience,it has poor ability in self-learning or adaptation.The self-learning capability of fuzzy inference system was realized in this paper using support vector machines(SVM),and a self-learning controller based on support vector machines-fuzzy inference system(SVM-FIS) was proposed.Both the structure and learning algorithms of the proposed self-learning controller were analyzed.Two learning algorithms of Multi-scaled Davidon-Fletcher-Powell(MDFP) method and chaotic optimization were compared.Simulation results for a nonlinear system demonstrate that the proposed self-learning controller has better control performance over fuzzy logic controller.