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A new type of simplified fuzzy rule-based systems

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A new type of simplified fuzzy rule-based systems. / Angelov, Plamen; Yager, Ronald.
In: International Journal of General Systems, Vol. 41, No. 2, 01.2012, p. 163-185.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Angelov, P & Yager, R 2012, 'A new type of simplified fuzzy rule-based systems', International Journal of General Systems, vol. 41, no. 2, pp. 163-185. https://doi.org/10.1080/03081079.2011.634807

APA

Angelov, P., & Yager, R. (2012). A new type of simplified fuzzy rule-based systems. International Journal of General Systems, 41(2), 163-185. https://doi.org/10.1080/03081079.2011.634807

Vancouver

Angelov P, Yager R. A new type of simplified fuzzy rule-based systems. International Journal of General Systems. 2012 Jan;41(2):163-185. doi: 10.1080/03081079.2011.634807

Author

Angelov, Plamen ; Yager, Ronald. / A new type of simplified fuzzy rule-based systems. In: International Journal of General Systems. 2012 ; Vol. 41, No. 2. pp. 163-185.

Bibtex

@article{265ce196d309418e85d2afc92314f208,
title = "A new type of simplified fuzzy rule-based systems",
abstract = "Over the last quarter of a century, two types of fuzzy rule-based (FRB) systemsdominated, namely Mamdani and Takagi–Sugeno type. They use the same type ofscalar fuzzy sets defined per input variable in their antecedent part which areaggregated at the inference stage by t-norms or co-norms representing logical AND/OR operations. In this paper, we propose a significantly simplified alternative to define the antecedent part of FRB systems by data Clouds and density distribution. This new type of FRB systems goes further in the conceptual and computational simplification while preserving the best features (flexibility, modularity, and human intelligibility) of its predecessors. The proposed concept offers alternative non-parametric form of the rules antecedents, which fully reflects the real data distribution and does not require any explicit aggregation operations and scalar membership functions to be imposed.Instead, it derives the fuzzy membership of a particular data sample to a Cloud by the data density distribution of the data associated with that Cloud. Contrast this to the clustering which is parametric data space decomposition/partitioning where the fuzzy membership to a cluster is measured by the distance to the cluster centre/prototype ignoring all the data that form that cluster or approximating their distribution. The proposed new approach takes into account fully and exactly the spatial distribution and similarity of all the real data by proposing an innovative and much simplified form of the antecedent part. In this paper, we provide several numerical examples aiming to illustrate the concept.",
keywords = "fuzzy rule-based systems, Mamdani and Takagi–Sugeno fuzzy systems, recursive least square estimation, data density and distribution, clustering",
author = "Plamen Angelov and Ronald Yager",
year = "2012",
month = jan,
doi = "10.1080/03081079.2011.634807",
language = "English",
volume = "41",
pages = "163--185",
journal = "International Journal of General Systems",
issn = "0308-1079",
publisher = "Taylor and Francis Ltd.",
number = "2",

}

RIS

TY - JOUR

T1 - A new type of simplified fuzzy rule-based systems

AU - Angelov, Plamen

AU - Yager, Ronald

PY - 2012/1

Y1 - 2012/1

N2 - Over the last quarter of a century, two types of fuzzy rule-based (FRB) systemsdominated, namely Mamdani and Takagi–Sugeno type. They use the same type ofscalar fuzzy sets defined per input variable in their antecedent part which areaggregated at the inference stage by t-norms or co-norms representing logical AND/OR operations. In this paper, we propose a significantly simplified alternative to define the antecedent part of FRB systems by data Clouds and density distribution. This new type of FRB systems goes further in the conceptual and computational simplification while preserving the best features (flexibility, modularity, and human intelligibility) of its predecessors. The proposed concept offers alternative non-parametric form of the rules antecedents, which fully reflects the real data distribution and does not require any explicit aggregation operations and scalar membership functions to be imposed.Instead, it derives the fuzzy membership of a particular data sample to a Cloud by the data density distribution of the data associated with that Cloud. Contrast this to the clustering which is parametric data space decomposition/partitioning where the fuzzy membership to a cluster is measured by the distance to the cluster centre/prototype ignoring all the data that form that cluster or approximating their distribution. The proposed new approach takes into account fully and exactly the spatial distribution and similarity of all the real data by proposing an innovative and much simplified form of the antecedent part. In this paper, we provide several numerical examples aiming to illustrate the concept.

AB - Over the last quarter of a century, two types of fuzzy rule-based (FRB) systemsdominated, namely Mamdani and Takagi–Sugeno type. They use the same type ofscalar fuzzy sets defined per input variable in their antecedent part which areaggregated at the inference stage by t-norms or co-norms representing logical AND/OR operations. In this paper, we propose a significantly simplified alternative to define the antecedent part of FRB systems by data Clouds and density distribution. This new type of FRB systems goes further in the conceptual and computational simplification while preserving the best features (flexibility, modularity, and human intelligibility) of its predecessors. The proposed concept offers alternative non-parametric form of the rules antecedents, which fully reflects the real data distribution and does not require any explicit aggregation operations and scalar membership functions to be imposed.Instead, it derives the fuzzy membership of a particular data sample to a Cloud by the data density distribution of the data associated with that Cloud. Contrast this to the clustering which is parametric data space decomposition/partitioning where the fuzzy membership to a cluster is measured by the distance to the cluster centre/prototype ignoring all the data that form that cluster or approximating their distribution. The proposed new approach takes into account fully and exactly the spatial distribution and similarity of all the real data by proposing an innovative and much simplified form of the antecedent part. In this paper, we provide several numerical examples aiming to illustrate the concept.

KW - fuzzy rule-based systems

KW - Mamdani and Takagi–Sugeno fuzzy systems

KW - recursive least square estimation

KW - data density and distribution

KW - clustering

U2 - 10.1080/03081079.2011.634807

DO - 10.1080/03081079.2011.634807

M3 - Journal article

VL - 41

SP - 163

EP - 185

JO - International Journal of General Systems

JF - International Journal of General Systems

SN - 0308-1079

IS - 2

ER -