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中文摘要：
不确定的高斯混合模型和二型Takagi—Sugeno—Kang（TSK）模糊模型之间的对应关系被建立：任何一个不确定的高斯混合模型都唯一对应着一个二型TSK模糊系统，不确定的高斯混合模型的条件均值和二型TSK模糊系统的输出是等价的．基于此，一种设计二型模糊系统的新方法被提出：通过建立不确定的高斯混合模型确定二型TSK模糊系统，即用概率统计的方法设计二型模糊系统．仿真实验结果表明利用不确定高斯混合模型设计的二型模糊系统比其它模型具有更强的抗噪性和更快的速度．
英文摘要：
This work explores how the uncertain Gaussian mixture model（UGMM） can be translated to an additive type-2 TSK（Takagi-Sugeno-Kang） fuzzy logic system. The mathematical equivalence between the conditional mean of a UGMM and the defuzzified output of a type-2 TSK fuzzy model（T2-TSK-FM） is proved. The relationship between a UGMM and a T2-TSK-FM, and the conditions for UGMM to T2-TSK- FM translation is made explicit in the form of a theorem. The proposed results provide a new method for constructing a T2-TSK-FM by interpreting a fuzzy system from a probabilistic viewpoint. Instead of estimating the parameters of the fuzzy rules directly, the parameters of a UGMM are estimated using any popular density estimation algorithm, such as expectation maximization. The proposed approach is also applied to Mackey-Glass chaotic time series. After comparing the simulation results with those obtained with other system modeling tools, it can be claimed that successful results are achieved.
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