Dynamic non-Singleton fuzzy logic systems for nonlinear modeling
Dynamic non-Singleton fuzzy logic systems for nonlinear modeling
复制标题
用于非线性建模的动态非单例模糊逻辑系统
DOI：
10.1109/91.580795
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
J. Mendel
中科院分区：
文献类型：
--
作者：
G. C. Mouzouris;J. Mendel
我们研究了模糊逻辑系统（FLSs）的动态版本，特别是它们的非单例泛化（NSFLSs），并推导了一种动态学习算法来训练系统参数。动态系统的历史敏感输出使它们在对未知顺序的过程建模方面比静态系统具有显著的优势。通过一个非线性动态系统辨识实例说明了这一点。由于动态NSFLS可以被认为是一般非线性自回归移动平均（NARMA）模型的一种，因此它能够简洁地模拟NARMA过程。我们研究了动态和静态FLSs在NARMA过程预测建模中的性能。
We investigate dynamic versions of fuzzy logic systems (FLSs) and, specifically, their non-Singleton generalizations (NSFLSs), and derive a dynamic learning algorithm to train the system parameters. The history-sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. This is illustrated through an example in nonlinear dynamic system identification. Since dynamic NSFLS's can be considered to belong to the family of general nonlinear autoregressive moving average (NARMA) models, they are capable of parsimoniously modeling NARMA processes. We study the performance of both dynamic and static FLSs in the predictive modeling of a NARMA process.