A Novel Fuzzy Modeling Structure-Decomposed Fuzzy System
提出一种分解模糊系统（DFS），将每个模糊变量分解为多个组件模糊子系统，使参数学习相互独立，从而加快收敛速度并降低测试误差，适用于动态系统建模。
Decomposed fuzzy system (DFS) is a fuzzy system with a novel structure. Due to its excellent learning performance, DFS is originally proposed for an online learning control scheme and is shown to have effective learning performance. This paper is about the use of DFS for modeling dynamic systems. Since the learning mechanism used in online learning control is not suitable for modeling tasks, a commonly used back propagation learning algorithm is adapted for the use of DFS in modeling dynamic systems. The structure of DFS is to decompose each fuzzy variable into fuzzy subsystems that are called component fuzzy systems. Owing to the independency among component fuzzy systems, the learning for those parameters is also independent among different component fuzzy systems and thus, the learning can become more efficient. From the simulation results, it is evident that the proposed DFS can have much faster convergent speed. In addition, the DFS has a smaller testing error than those of other fuzzy systems.