@inproceedings{b15e93749a094f1687e7143d72183e11,
title = "Systems identification using type-2 fuzzy neural network (type-2 FNN) systems",
abstract = "This paper presents a type-2 fuzzy neural network system (type-2 FNN) and its learning algorithm using back-propagation algorithm. In our previous results, the FNN system using type-1 fuzzy logic systems (FLS) is called type-1 FNN system. It has the properties of parallel computation scheme, easy to implement, fuzzy logic inference system, and parameters convergence. For considering the fuzzy rules uncertainties, we use the type-2 FLSs to develop a type-2 FNN system. The type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-based fuzzy logic systems (FLSs). In this paper, the previous results of type-1 FNN are extended to a type-2 one. In addition, the corresponding learning algorithm is derived by back-program algorithm. Several examples are presented to illustrate the effectiveness of our approach.",
keywords = "fuzzy neural network, type-2 fuzzy sets back-propagation algorithm",
author = "Lee, {Ching Hung} and Lin, {Yu Ching} and Lai, {Wei Yu}",
year = "2003",
doi = "10.1109/CIRA.2003.1222178",
language = "English",
series = "Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1264--1269",
booktitle = "Proceedings - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation",
address = "United States",
note = "null ; Conference date: 16-07-2003 Through 20-07-2003",
}