Title :
A new class of fuzzy neural networks with application in system modeling
Author :
Ma, H. ; El-Keib, A.A. ; Ma, X.
Author_Institution :
Dept. of Electr. Eng., Alabama Univ., Tuscaloosa, AL, USA
Abstract :
This paper presents a new class of fuzzy neural networks and its application to system modeling. The optimal set of fuzzy associative memory rules and weights can be identified for a given set of sample data. Test results of modeling a nonlinear system show that the proposed fuzzy neural network can accurately model a complicated nonlinear system and the model is quite robust in the sense that its modeling accuracy is much insensitive to sample data
Keywords :
digital simulation; neural nets; nonlinear systems; fuzzy associative memory rules; fuzzy neural networks; modeling accuracy; nonlinear system; sample data; system modeling; Artificial neural networks; Associative memory; Cellular neural networks; Fuzzy control; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Intelligent networks; Modeling; Nonlinear systems;
Conference_Titel :
Southeastcon '94. Creative Technology Transfer - A Global Affair., Proceedings of the 1994 IEEE
Conference_Location :
Miami, FL
Print_ISBN :
0-7803-1797-1
DOI :
10.1109/SECON.1994.324333
