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中文摘要：
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一模糊推理系统，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一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。
英文摘要：
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.
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