支持向量机-模糊推理自学习控制器设计

常规的模糊推理系统大多由专家经验建立模糊规则,自学习能力不强.提出了一种支持向量机-模糊推理系统,由支持向量机实现模糊推理系统的自学习,并设计了一种支持向量机-模糊推理自学习控制器.文章给出了自学习控制器的结构和学习算法,对比研究了变尺度梯度优化和混沌优化两种学习算法.针对非线性对象的仿真实验验证了该控制器的优良性能,控制效果比模糊逻辑控制器更好.

Self-learning controller using support vector machines and fuzzy inference system

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.