基于压缩集密度估计的ML模糊推理系统构建

Reduced Set Density Estimation Based ML Fuzzy Inference Systems Construction

基于高斯混合模型揭示了ML模糊推理系统构建可以等价为压缩集密度估计问题。利用此发现提出基于压缩集密度估计器RSDE的ML模糊推理系统训练算法。该算法有如下特点:①无需人为设定模糊规则数目;②是一个二次优化问题,可利用快速的二次规划算法快速求解。通过模拟和真实数据集验证,实验结果亦证实了上述优点。

Based on the Gaussian mixture Inference system （MLFIS） construction Estimation problem. Then by using this model, it is revealed that the ML （Mamdani-Larsen） Fuzzy can be equivalently taken as the Reduced Set density finding a Reduced Set density Estimator （RS.DE） based MLFIS training algorithm is presented. The proposed algorithm has the following distinctive characteristics ： （1）The number of fuzzy rules is not neseccery to be set manually ; （2）It is essentially a QP propblem and can be solved directly with fast QP algorithms. The above virtues are confirmed with several experiments on synthetic and real-word datasets.