Study of FCM Model for Fault Clustering Using Optimization Method

DOI

10.2991/itms-15.2015.164How to use a DOI?

Keywords

Fuzzy Cognitive Map (FCM); Prediction; Fault; Clustering; Optimization

Abstract

In this paper, Fuzzy Cognitive Map (FCM) is employed and discussed to analyze time series that represents fault dynamics. At first, the essential of FCM prototype is shown, and diversified data set that are produced from the prototype to construct candidate FCM model is proposed. The Particle Swarm Optimization (PSO) and Simulated Annealing (SA) learning algorithm is taken as optimization method based on computational intelligence. Secondly, analysis of representation for time series from real world application is carried out with the proposed FCM model to assess the quality of FCM design methodology and algorithmic performance. The dynamics of time series is depicted by fuzzy c-mean clustering, and the concept of the node in the candidate FCM model corresponds to the activation level of each cluster center. In the end, the parameters of node number in the candidate FCM model and steepness of activation function are taken into consideration in order to obtain the better result. The discussion and findings are offered and the results show that the proposed method is desirable in the representation of time series both at numeric value level and linguistic level.

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  - Haibin Yuan
AU  - Jiaojiao Xu
PY  - 2015/11
DA  - 2015/11
TI  - Study of FCM Model for Fault Clustering Using Optimization Method
BT  - Proceedings of the 2015 International Conference on Industrial Technology and Management Science
PB  - Atlantis Press
SP  - 695
EP  - 698
SN  - 2352-538X
UR  - https://doi.org/10.2991/itms-15.2015.164
DO  - 10.2991/itms-15.2015.164
ID  - Yuan2015/11
ER  -