为提高非线性复杂系统状态估计的效率与精度，提出一种基于多因素模糊综合评判的变结构多模型方法（ MFIE MM）。MFIE MM 首先确定模型全集并提取各个模型的公共因素，进而选择模糊综合鉴别函数构建模糊评价集合；其次单因素模糊评判矩阵和多因素模糊评判准则得到各个模型的相似度；最后选出当前时刻最佳模型并以此模型为区域中心实时生成参与状态估计的模型集合。仿真结果显示，由 MFIE MM 处理得到的位置变量估计误差协方差从2.15 m降到2.05 m，单拍处理时间从0.0027 s 降到0.0018 s。因此，MFIE MM 在显著提高算法精度的同时有效降低了算法运行时间和模型平均误差。%A variable structure multi-model method based on the multi-factor fuzzy integrated evaluation(MFIE MM)is presented to improve the estimation precision and efficiency for nonlinear hybrid systems. MFIE MM ini-tially ascertains the total model set and extracts the common factors of all the models,then chooses the fuzzy syn-thetic discriminant function and constructs the fuzzy evaluation set. After this,the similarity of every model through calculating the single-factor fuzzy judge matrix and the multi-factor fuzzy judge criterion is obtained. Finally it se-lects the best model at the current time and centers on this model as the regional center to produce in time the mod-el set which joins in the state estimation. The simulation results show that the estimation error variances belonging to the position of MFIE MM are improved from 2.15 m to 2.05 m and the time of one cycle is decreased from 0.002 7 s to 0.001 8 s. So a conclusion is derived that MFIE MM evidently improves the precision,shortens the running time of the algorithm,and effectively reduces the average error rate of the model.
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