Paper Info
Title
Neural Network With Lower And Upper Type-2 Fuzzy Weights Using The Backpropagation Learning Method
Abstract
In this paper the lower and upper type-2 fuzzy weight adjustment applied in a neural network performing the learning method is proposed. The mathematical representation of the adaptation of the interval type-2 fuzzy weights and the proposed learning method architecture are presented. This research is based in the analysis of the recent methods that manage weight adaptation and implementing this analysis in the adaptation of these methods with type-2 fuzzy weights. In this paper, we work with type-2 fuzzy weights lower and upper in the neural network architecture and the lower and upper final results obtained are presented in the final. The proposed approach is applied to a case of Mackey-Glass time series prediction.
Year
DOI
Venue
2013
10.1109/IFSA-NAFIPS.2013.6608475
PROCEEDINGS OF THE 2013 JOINT IFSA WORLD CONGRESS AND NAFIPS ANNUAL MEETING (IFSA/NAFIPS)
Keywords
Field
DocType
Neural Networks, Type-2 Fuzzy Weights, Backpropagation Algorithm, Type-2 fuzzy system
Neuro-fuzzy,Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy logic,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy control system,Fuzzy number
Conference
Citations 
PageRank 
References 
0
0.34
24
Authors
3
Name
Order
Citations
PageRank
Fernando Gaxiola11358.42
Patricia Melin24009259.43
Fevrier Valdez395257.96