Paper Info
Title
Real-Time Recurrent Interval Type-2 Fuzzy-Neural System Identification Using Uncertainty Bounds
Abstract
In this paper, a novel real-time recurrent interval type-2 fuzzy neural system identification is presented using intelligent algorithm. Interval type-2 fuzzy neural network (FNN) by adding feedback connection on the input layer is introduced to handle uncertainties which arise from the noisy training data, noisy measurements used to activate the fuzzy logic system (FLS) and linguistic uncertainties. In order to overcome the iterative type-reduction overhead, the intelligent algorithms are proposed using uncertainty bounds, inner- and outer-bound sets, which provide estimates of the uncertainties contained in the output of an interval type-2 FLS without having to perform the costly computations of type-reduction. Duffing forced oscillation system is fully illustrated to be identified and simulation results show that not only similar identification performance to one that use type-reduction can be achieved but also significantly faster real-time identification can be performed.
Year
DOI
Venue
2012
10.1109/FUZZ-IEEE.2012.6251356
2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
Keywords
Field
DocType
Uncertainty bound, interval type-2 FLS, real-time, system identification, type-reduction, recurrent
Iterative method,Control theory,Computer science,Fuzzy logic,Computational linguistics,Type theory,Fuzzy set,Artificial intelligence,System identification,Artificial neural network,Machine learning,Computation
Conference
ISSN
Citations 
PageRank 
1098-7584
0
0.34
References 
Authors
12
3
Name
Order
Citations
PageRank
Tsung-Chih Lin136126.73
Chia-Hao Kuo233.09
Valentina Emilia Balas372.50