Abstract
This paper describes the development of Interval Type-2 NEO-Fuzzy Neural Network for modeling of complex dynamics. The proposed network represents a parallel set of multiple zero order Sugeno type approximations, related only to their own input argument. The induced gradient based learning procedure, adjusts solely the consequent network parameters. To improve the robustness of the network and the possibilities for handling uncertainties, Type-2 Gaussian fuzzy sets are introduced into the network topology. The potentials of the proposed approach in modeling of Mackey-Glass and Rossler Chaotic time series are studied.
Original language | English |
---|---|
Title of host publication | Artificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings |
Publisher | Springer |
Pages | 643-650 |
Number of pages | 8 |
ISBN (Print) | 9783319111780 |
DOIs | |
Publication status | Published - 2014 |
MoE publication type | A4 Conference publication |
Event | International Conference on Artificial Neural Networks - Hamburg, Germany Duration: 15 Sept 2014 → 19 Sept 2014 Conference number: 24 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|
Volume | 8681 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on Artificial Neural Networks |
---|---|
Abbreviated title | ICANN |
Country/Territory | Germany |
City | Hamburg |
Period | 15/09/2014 → 19/09/2014 |
Keywords
- chaotic time-series prediction
- dynamic modeling
- neo-fuzzy neuron
- neural networks
- type-2 fuzzy set