Home > Research > Publications & Outputs > Fuzzily Connected Multimodel Systems Evolving A...
View graph of relations

Fuzzily Connected Multimodel Systems Evolving Autonomously From Data Streams

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

Fuzzily Connected Multimodel Systems Evolving Autonomously From Data Streams. / Angelov, Plamen.
In: IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol. 41, No. 4, 08.2011, p. 898-910.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Angelov, P 2011, 'Fuzzily Connected Multimodel Systems Evolving Autonomously From Data Streams', IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 41, no. 4, pp. 898-910. https://doi.org/10.1109/TSMCB.2010.2098866

APA

Angelov, P. (2011). Fuzzily Connected Multimodel Systems Evolving Autonomously From Data Streams. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 41(4), 898-910. https://doi.org/10.1109/TSMCB.2010.2098866

Vancouver

Angelov P. Fuzzily Connected Multimodel Systems Evolving Autonomously From Data Streams. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics. 2011 Aug;41(4):898-910. doi: 10.1109/TSMCB.2010.2098866

Author

Angelov, Plamen. / Fuzzily Connected Multimodel Systems Evolving Autonomously From Data Streams. In: IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics. 2011 ; Vol. 41, No. 4. pp. 898-910.

Bibtex

@article{c68e86a6fd2b4ab9909a23158a9d28a8,
title = "Fuzzily Connected Multimodel Systems Evolving Autonomously From Data Streams",
abstract = "A general framework and a holistic concept are proposed in this paper that combine computationally light machine learning from streaming data with the online identification and adaptation of dynamic systems in regard 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 thrust 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 detect and react to the shift in the data distribution by evolving the system structure. Novelties of this contribution lie in the following: 1) the computationally simple approach (simpl_e_Clustering-simplified evolving Clustering) to data space partitioning by recursive evolving clustering based on the relative position of the new data sample to the mean of the overall data, 2) the learning technique for online structure evolution as a reaction to the shift in the data distribution, 3) the method for online system structure simplification based on utility and inputs/feature selection, and 4) the novel graphical illustration of the spatiotemporal evolution of the data stream. The application domain for this computationally efficient technique ranges from clustering, modeling, 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.",
keywords = "Evolving fuzzy systems, fuzzily weighted recursive least-squares estimation , fuzzy rule-based systems",
author = "Plamen Angelov",
year = "2011",
month = aug,
doi = "10.1109/TSMCB.2010.2098866",
language = "English",
volume = "41",
pages = "898--910",
journal = "IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics",
issn = "1083-4419",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

RIS

TY - JOUR

T1 - Fuzzily Connected Multimodel Systems Evolving Autonomously From Data Streams

AU - Angelov, Plamen

PY - 2011/8

Y1 - 2011/8

N2 - A general framework and a holistic concept are proposed in this paper that combine computationally light machine learning from streaming data with the online identification and adaptation of dynamic systems in regard 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 thrust 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 detect and react to the shift in the data distribution by evolving the system structure. Novelties of this contribution lie in the following: 1) the computationally simple approach (simpl_e_Clustering-simplified evolving Clustering) to data space partitioning by recursive evolving clustering based on the relative position of the new data sample to the mean of the overall data, 2) the learning technique for online structure evolution as a reaction to the shift in the data distribution, 3) the method for online system structure simplification based on utility and inputs/feature selection, and 4) the novel graphical illustration of the spatiotemporal evolution of the data stream. The application domain for this computationally efficient technique ranges from clustering, modeling, 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.

AB - A general framework and a holistic concept are proposed in this paper that combine computationally light machine learning from streaming data with the online identification and adaptation of dynamic systems in regard 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 thrust 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 detect and react to the shift in the data distribution by evolving the system structure. Novelties of this contribution lie in the following: 1) the computationally simple approach (simpl_e_Clustering-simplified evolving Clustering) to data space partitioning by recursive evolving clustering based on the relative position of the new data sample to the mean of the overall data, 2) the learning technique for online structure evolution as a reaction to the shift in the data distribution, 3) the method for online system structure simplification based on utility and inputs/feature selection, and 4) the novel graphical illustration of the spatiotemporal evolution of the data stream. The application domain for this computationally efficient technique ranges from clustering, modeling, 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.

KW - Evolving fuzzy systems

KW - fuzzily weighted recursive least-squares estimation

KW - fuzzy rule-based systems

UR - http://www.scopus.com/inward/record.url?scp=79960697024&partnerID=8YFLogxK

U2 - 10.1109/TSMCB.2010.2098866

DO - 10.1109/TSMCB.2010.2098866

M3 - Journal article

VL - 41

SP - 898

EP - 910

JO - IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics

JF - IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics

SN - 1083-4419

IS - 4

ER -