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Soft Computing
Erschienen in: Soft Computing 6/2014

01.06.2014 | Methodologies and Application

Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction

verfasst von: Oscar Castillo, Juan R. Castro, Patricia Melin, Antonio Rodriguez-Diaz

Erschienen in: Soft Computing | Ausgabe 6/2014

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Abstract

Neural networks (NNs), type-1 fuzzy logic systems and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to be important methods in real world applications, which range from pattern recognition, time series prediction, to intelligent control. Recent research shows that embedding an IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect or incomplete information. In this paper we are presenting several models of interval type-2 fuzzy neural networks (IT2FNNs) that use a set of rules and interval type-2 membership functions for that purpose. Simulation results of non-linear function identification using the IT2FNN for one and three variables and for the Mackey–Glass chaotic time series prediction are presented to illustrate that the proposed models have potential for real world applications.
Vorheriger Artikel Utilizing time-linkage property in DOPs: An information sharing based Artificial Bee Colony algorithm for tracking multiple optima in uncertain environments
Nächster Artikel Asynchronous and implicitly parallel evolutionary computation models

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Metadaten
Titel
Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction
verfasst von
Oscar Castillo
Juan R. Castro
Patricia Melin
Antonio Rodriguez-Diaz
Publikationsdatum
01.06.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 6/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-013-1139-y

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