基于目标函数的模糊模型一体化建模

基于模糊集合的模糊模型, 利用模糊推理规则描述复杂、病态、非线性系统是一种有效方法. 本文提出了利用目标函数确定非线性系统的结构和参数的方法. 首先, 通过Gustafson-Kessel(GK)模糊聚类确定模型结构. 然后, 通过目标函数与参数估计一起进行递推计算, 进而实现对模糊模型结构简化, 删除冗余规则. 结构确定过程中采用了UD矩阵分解方法, 大大降低了计算量. 仿真结果证明了提出方法的有效性.

An integrated modeling algorithm of fuzzy model using objective function

For dynamic systems with complex, ill-conditioned or nonlinear characteristics, the fuzzy modeling method based on fuzzy sets is very effective to describe the properties of the systems. By using objective function, we propose a new algorithm to confirm the structure and parameters of fuzzy model for nonlinear systems. First, the structure of fuzzy model is confirmed by using Gustafson-Kessel(GK) fuzzy clustering; and then, the objective function and parameter estimation are simultaneously employed in recursive calculations to simply the model structure and delete redundant rules. The UD matrix decomposition is used to reduce the amount of computation in the determination process of fuzzy model. The simulation results demonstrate the effectiveness of the proposed method.