DocumentCode
3442965
Title
A simplified learning algorithm for interval type-2 fuzzy neural network
Author
Chen, Liuyuan ; Mu, Xiaoxia ; Wang, Hongjun ; Li, Wenlin
Author_Institution
Sch. of Inf. Eng., Wuhan Univ. of Technol., Wuhan, China
Volume
2
fYear
2010
fDate
29-31 Oct. 2010
Firstpage
684
Lastpage
688
Abstract
This paper is devoted to the learning problem for the interval type-2 fuzzy neural network. The type-reduced set of the proposed neural network is firstly estimated by the linear combination of boundary type-1 fuzzy logic systems, and then the corresponding output estimation error is analyzed. Finally, a novel risk function is represented and a simplified back propagation learning algorithm is developed which can largely relieve the computation burden.
Keywords
backpropagation; fuzzy logic; fuzzy neural nets; learning (artificial intelligence); back propagation learning algorithm; boundary type-1 fuzzy logic systems; interval type-2 fuzzy neural network; learning algorithm; type reduced set; Equations; Fuzzy neural network (FNN); interval type-2 fuzzy neural network (T2FNN); type reduction;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on
Conference_Location
Xiamen
Print_ISBN
978-1-4244-6582-8
Type
conf
DOI
10.1109/ICICISYS.2010.5658465
Filename
5658465
Link To Document