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Evolving fuzzy systems from data streams in real-time

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Evolving fuzzy systems from data streams in real-time. / Angelov, Plamen; Zhou, Xiaowei.
Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems. ed. / Plamen Angelov. England: IEEE, 2006. p. 29-35.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Angelov, P & Zhou, X 2006, Evolving fuzzy systems from data streams in real-time. in P Angelov (ed.), Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems. IEEE, England, pp. 29-35, 2006 IEEE Symposium on Evolving Fuzzy Systems, Ambleside, Lake District, UK, 7/09/06. https://doi.org/10.1109/ISEFS.2006.251157

APA

Angelov, P., & Zhou, X. (2006). Evolving fuzzy systems from data streams in real-time. In P. Angelov (Ed.), Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems (pp. 29-35). IEEE. https://doi.org/10.1109/ISEFS.2006.251157

Vancouver

Angelov P, Zhou X. Evolving fuzzy systems from data streams in real-time. In Angelov P, editor, Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems. England: IEEE. 2006. p. 29-35 doi: 10.1109/ISEFS.2006.251157

Author

Angelov, Plamen ; Zhou, Xiaowei. / Evolving fuzzy systems from data streams in real-time. Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems. editor / Plamen Angelov. England : IEEE, 2006. pp. 29-35

Bibtex

@inproceedings{65e4f9be151646aca1d8be0e396905b3,
title = "Evolving fuzzy systems from data streams in real-time",
abstract = "An approach to real-time generation of fuzzy rule-base systems of extended Takagi-Sugeno (xTS) type from data streams is proposed in the paper. The xTS fuzzy system combines both zero and first order Takagi-Sugeno (TS) type systems. The fuzzy rule-base (system structure) evolves starting 'from scratch' based on the data distribution in the joint input/output data space. An incremental clustering procedure that takes into account the non-stationary nature of the data pattern and generates clusters that are used to form fuzzy rule based systems antecedent part in on-line mode is used as a first stage of the non-iterative learning process. This structure proved to be computationally efficient and powerful to represent in a transparent way complex non-linear relationships. The decoupling of the learning task into a non-iterative, recursive (thus computationally very efficient and applicable in real-time) clustering with a modified version of the well known recursive parameter estimation technique leads to a very powerful construct - evolving xTS (exTS). It is transparent and linguistically interpretable. The contributions of this paper are: i) introduction of an adaptive recursively updated radius of the clusters (zone of influence of the fuzzy rules) that learns the data distribution/variance/scatter in each cluster; ii) a new condition to replace clusters that excludes contradictory rules; iii) an extended formulation that includes both zero order TS and simplified Mamdani multi-input-multi-output (MIMO) systems; iv) new improved formulation of the membership functions, which closer resembles the normal Gaussian distribution; v) introduction of measures of clusters quality that are used to form the antecedent parts of respective fuzzy rules, namely their age and support; vi) experimental results with a well known benchmark problem as well as with real experimental data of concentration of exhaust gases (NOx) in on-line modeling of car engine test rigs",
author = "Plamen Angelov and Xiaowei Zhou",
note = "Best paper runner up award. {"}{\textcopyright}2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.{"} {"}This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.{"}; 2006 IEEE Symposium on Evolving Fuzzy Systems ; Conference date: 07-09-2006 Through 09-09-2006",
year = "2006",
month = sep,
day = "7",
doi = "10.1109/ISEFS.2006.251157",
language = "English",
isbn = "0780397193",
pages = "29--35",
editor = "Plamen Angelov",
booktitle = "Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Evolving fuzzy systems from data streams in real-time

AU - Angelov, Plamen

AU - Zhou, Xiaowei

N1 - Best paper runner up award. "©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."

PY - 2006/9/7

Y1 - 2006/9/7

N2 - An approach to real-time generation of fuzzy rule-base systems of extended Takagi-Sugeno (xTS) type from data streams is proposed in the paper. The xTS fuzzy system combines both zero and first order Takagi-Sugeno (TS) type systems. The fuzzy rule-base (system structure) evolves starting 'from scratch' based on the data distribution in the joint input/output data space. An incremental clustering procedure that takes into account the non-stationary nature of the data pattern and generates clusters that are used to form fuzzy rule based systems antecedent part in on-line mode is used as a first stage of the non-iterative learning process. This structure proved to be computationally efficient and powerful to represent in a transparent way complex non-linear relationships. The decoupling of the learning task into a non-iterative, recursive (thus computationally very efficient and applicable in real-time) clustering with a modified version of the well known recursive parameter estimation technique leads to a very powerful construct - evolving xTS (exTS). It is transparent and linguistically interpretable. The contributions of this paper are: i) introduction of an adaptive recursively updated radius of the clusters (zone of influence of the fuzzy rules) that learns the data distribution/variance/scatter in each cluster; ii) a new condition to replace clusters that excludes contradictory rules; iii) an extended formulation that includes both zero order TS and simplified Mamdani multi-input-multi-output (MIMO) systems; iv) new improved formulation of the membership functions, which closer resembles the normal Gaussian distribution; v) introduction of measures of clusters quality that are used to form the antecedent parts of respective fuzzy rules, namely their age and support; vi) experimental results with a well known benchmark problem as well as with real experimental data of concentration of exhaust gases (NOx) in on-line modeling of car engine test rigs

AB - An approach to real-time generation of fuzzy rule-base systems of extended Takagi-Sugeno (xTS) type from data streams is proposed in the paper. The xTS fuzzy system combines both zero and first order Takagi-Sugeno (TS) type systems. The fuzzy rule-base (system structure) evolves starting 'from scratch' based on the data distribution in the joint input/output data space. An incremental clustering procedure that takes into account the non-stationary nature of the data pattern and generates clusters that are used to form fuzzy rule based systems antecedent part in on-line mode is used as a first stage of the non-iterative learning process. This structure proved to be computationally efficient and powerful to represent in a transparent way complex non-linear relationships. The decoupling of the learning task into a non-iterative, recursive (thus computationally very efficient and applicable in real-time) clustering with a modified version of the well known recursive parameter estimation technique leads to a very powerful construct - evolving xTS (exTS). It is transparent and linguistically interpretable. The contributions of this paper are: i) introduction of an adaptive recursively updated radius of the clusters (zone of influence of the fuzzy rules) that learns the data distribution/variance/scatter in each cluster; ii) a new condition to replace clusters that excludes contradictory rules; iii) an extended formulation that includes both zero order TS and simplified Mamdani multi-input-multi-output (MIMO) systems; iv) new improved formulation of the membership functions, which closer resembles the normal Gaussian distribution; v) introduction of measures of clusters quality that are used to form the antecedent parts of respective fuzzy rules, namely their age and support; vi) experimental results with a well known benchmark problem as well as with real experimental data of concentration of exhaust gases (NOx) in on-line modeling of car engine test rigs

U2 - 10.1109/ISEFS.2006.251157

DO - 10.1109/ISEFS.2006.251157

M3 - Conference contribution/Paper

SN - 0780397193

SP - 29

EP - 35

BT - Proceedings of the 2006 International Symposium on Evolving Fuzzy Systems

A2 - Angelov, Plamen

PB - IEEE

CY - England

T2 - 2006 IEEE Symposium on Evolving Fuzzy Systems

Y2 - 7 September 2006 through 9 September 2006

ER -