A proposal for regime change/duration classification in chaotic systems
- 10.2991/ifsa-eusflat-15.2015.201How to use a DOI?
- Chaotic systems, fuzzy clustering, bred vectors, Lorenz attractor, neuro-fuzzy systems, decision trees.
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.
- © 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/).
Cite this article
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 -