A proposal for regime change/duration classification in chaotic systems

DOI

10.2991/ifsa-eusflat-15.2015.201How to use a DOI?

Keywords

Chaotic systems, fuzzy clustering, bred vectors, Lorenz attractor, neuro-fuzzy systems, decision trees.

Abstract

In order to to predict regime duration in a given chaotic system, for a set of output prototypes are available, we propose to use a clustering technique for the definition of classes of regime duration, which are then used by a chosen classifier. In this way, the exact boundaries between classes are allowed to emerge from the data, as long as prototypical values fall in distinct classes. We investigate the use of both unsupervised and semi-supervised fuzzy clustering techniques FCM and ssFCM, as well as the traditional k-Means technique. To classify the data, we use neuro-fuzzy system ANFIS and two decision trees (J48 and NBTree). We apply the procedure on the well-known Lorenz strange attractor, having bred vector counts as input variables.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Priscilla Lopes
AU  - Ivana Yoshie Sumida
AU  - Heloisa A. Camargo
AU  - Haroldo De Campos Velho
AU  - Sandra Sandri
PY  - 2015/06
DA  - 2015/06
TI  - A proposal for regime change/duration classification in chaotic systems
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 1419
EP  - 1426
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.201
DO  - 10.2991/ifsa-eusflat-15.2015.201
ID  - Lopes2015/06
ER  -