Angelov, Plamen (2011) Fuzzily connected multi-model systems evolving autonomously from data streams. IEEE Transactions on Systems Man and Cybernetics, 41 (4). pp. 898-910. ISSN 1094-6977
Full text not available from this repository.Abstract
A general framework and a holistic concept are proposed in this paper that combine computationally light machine learning from streaming data with the on-line identi- fication and adaptation of dynamic systems in regards to their structure and parameters. According to this concept, the system is assumed to be decomposable into a set of fuzzily connected simple local models. The main trust of this paper is in the development of an original approach for the self-design, self-monitoring, self-management, and self-learning of such systems in a dynamic manner from data streams which automatically detects and reacts to the shift in the data distribution by evolving the system structure. Novelties of this contribution lie in; a) the computationally simple approach (simpl_e_Clustering – simplified evolving Clustering) to data space partitioning by recursive evolving clustering based on the relative position to the mean of the overall data; b) the learning technique for on-line structure evolution as a reaction to the shift in the data distribution; c) the method for on-line system structure simplification based on utility and inputs/feature selection; d) the novel graphical illustration of the spatio-temporal evolution of the data stream. The application domain for this computationally efficient technique ranges from clustering, modelling, prognostics, classification and time-series prediction to pattern recognition, image segmentation, vector quantization etc. to more general problems in various application areas, e.g. intelligent sensors, mobile robotics, advanced manufacturing processes, etc. (c) IEEE Press
| Item Type: | Article |
|---|---|
| Journal or Publication Title: | IEEE Transactions on Systems Man and Cybernetics |
| Uncontrolled Keywords: | fuzzy rule-based systems ; fuzzily weighted recursive least squares estimation ; evolving fuzzy systems |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Departments: | Faculty of Science and Technology > School of Computing & Communications |
| ID Code: | 34372 |
| Deposited By: | Dr. Plamen Angelov |
| Deposited On: | 13 Oct 2010 14:39 |
| Refereed?: | Yes |
| Published?: | Published |
| Last Modified: | 26 Jul 2012 17:36 |
| Identification Number: | |
| URI: | http://eprints.lancs.ac.uk/id/eprint/34372 |
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