Title :
Nonlinear predictive modeling using dynamic non-singleton fuzzy logic systems
Author :
Mouzouris, George C. ; Mendel, Jerry M.
Author_Institution :
Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
We investigate the dynamic versions of fuzzy logic systems (FLSs), and specifically their nonsingleton 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. Since dynamic NSFLSs 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
Keywords :
adaptive systems; autoregressive moving average processes; fuzzy logic; fuzzy systems; generalisation (artificial intelligence); learning systems; modelling; nonlinear dynamical systems; NARMA models; adaptive systems; dynamic learning algorithm; dynamic nonsingleton fuzzy logic systems; nonlinear autoregressive moving average models; nonlinear predictive modeling; nonsingleton generalizations; Backpropagation algorithms; Fuzzy logic; Fuzzy systems; Image processing; Neural networks; Nonlinear dynamical systems; Output feedback; Predictive models; Signal processing; Uncertainty;
Conference_Titel :
Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-3645-3
DOI :
10.1109/FUZZY.1996.552351
