Genetic Granular Neural Networks

Authors:
Yan-Qing Zhang;Bo Jin;Yuchun Tang
Affiliations:
Department of Computer Science, Georgia State University, Atlanta, GA 30302-3994, USA;Department of Computer Science, Georgia State University, Atlanta, GA 30302-3994, USA;Secure Computing Corporation, Alpharetta, GA 30022, USA
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

Citing 9
Cited 0

Applications of type-2 fuzzy logic systems to forecasting of time-series

Information Sciences—Informatics and Computer Science: An International Journal
Granular computing: an emerging paradigm

Granular computing: an emerging paradigm
Type-2 Fuzzy Web Shopping Agents

WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Type-2 fuzzy logic systems

IEEE Transactions on Fuzzy Systems
Interval type-2 fuzzy logic systems: theory and design

IEEE Transactions on Fuzzy Systems
Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems

IEEE Transactions on Fuzzy Systems
Computing derivatives in interval type-2 fuzzy logic systems

IEEE Transactions on Fuzzy Systems
Constructive granular systems with universal approximation and fast knowledge discovery

IEEE Transactions on Fuzzy Systems
Granular neural networks for numerical-linguistic data fusion and knowledge discovery

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

To make interval-valued granular reasoning efficiently and optimize interval membership functions based on training data effectively, a new Genetic Granular Neural Network (GGNN) is desinged. Simulation results have shown that the GGNN is able to extract useful fuzzy knowledge effectively and efficiently from training data to have high training accuracy.