Home > Research > Publications & Outputs > Autonomous Data Density pruning fuzzy neural ne...

Electronic data

  • EVOS

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s12530-020-09336-3

    Accepted author manuscript, 416 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print

Standard

Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network. / de Campos Souza, Paulo Vitor; Soares, Eduardo A.; Guimarães, Augusto Junio; Araujo, Vanessa Souza; Araujo, Vinicius Jonathan S.; Rezende, Thiago Silva.

In: Evolving Systems, Vol. 12, No. 4, 31.12.2021, p. 899-911.

Research output: Contribution to journalJournal articlepeer-review

Harvard

de Campos Souza, PV, Soares, EA, Guimarães, AJ, Araujo, VS, Araujo, VJS & Rezende, TS 2021, 'Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network', Evolving Systems, vol. 12, no. 4, pp. 899-911. https://doi.org/10.1007/s12530-020-09336-3

APA

de Campos Souza, P. V., Soares, E. A., Guimarães, A. J., Araujo, V. S., Araujo, V. J. S., & Rezende, T. S. (2021). Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network. Evolving Systems, 12(4), 899-911. https://doi.org/10.1007/s12530-020-09336-3

Vancouver

de Campos Souza PV, Soares EA, Guimarães AJ, Araujo VS, Araujo VJS, Rezende TS. Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network. Evolving Systems. 2021 Dec 31;12(4):899-911. https://doi.org/10.1007/s12530-020-09336-3

Author

de Campos Souza, Paulo Vitor ; Soares, Eduardo A. ; Guimarães, Augusto Junio ; Araujo, Vanessa Souza ; Araujo, Vinicius Jonathan S. ; Rezende, Thiago Silva. / Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network. In: Evolving Systems. 2021 ; Vol. 12, No. 4. pp. 899-911.

Bibtex

@article{1af6a71a11fd4a52a86133ad7a0dcda3,
title = "Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network",
abstract = "Traditionally, fuzzy neural networks have parametric clustering methods based on equally spaced membership functions to fuzzify inputs of the model. In this sense, it produces an excessive number calculations for the parameters{\textquoteright} definition of the network architecture, which may be a problem especially for real-time large-scale tasks. Therefore, this paper proposes a new model that uses a non-parametric technique for the fuzzification process. The proposed model uses an autonomous data density approach in a pruned fuzzy neural network, wich favours the compactness of the model. The performance of the proposed approach is evaluated through the usage of databases related to the Optical Interconnection Network. Finally, binary patterns classification tests for the identification of temporal distribution (asynchronous or client–server) were performed and compared with state-of-the-art fuzzy neural-based and traditional machine learning approaches. Results demonstrated that the proposed model is an efficient tool for these challenging classification tasks.",
keywords = "Autonomous data density, Fuzzy neural networks, Optical interconnection network",
author = "{de Campos Souza}, {Paulo Vitor} and Soares, {Eduardo A.} and Guimar{\~a}es, {Augusto Junio} and Araujo, {Vanessa Souza} and Araujo, {Vinicius Jonathan S.} and Rezende, {Thiago Silva}",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/s12530-020-09336-3",
year = "2020",
month = mar,
day = "20",
doi = "10.1007/s12530-020-09336-3",
language = "English",
volume = "12",
pages = "899--911",
journal = "Evolving Systems",
issn = "1868-6478",
publisher = "Springer Verlag",
number = "4",

}

RIS

TY - JOUR

T1 - Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

AU - de Campos Souza, Paulo Vitor

AU - Soares, Eduardo A.

AU - Guimarães, Augusto Junio

AU - Araujo, Vanessa Souza

AU - Araujo, Vinicius Jonathan S.

AU - Rezende, Thiago Silva

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s12530-020-09336-3

PY - 2020/3/20

Y1 - 2020/3/20

N2 - Traditionally, fuzzy neural networks have parametric clustering methods based on equally spaced membership functions to fuzzify inputs of the model. In this sense, it produces an excessive number calculations for the parameters’ definition of the network architecture, which may be a problem especially for real-time large-scale tasks. Therefore, this paper proposes a new model that uses a non-parametric technique for the fuzzification process. The proposed model uses an autonomous data density approach in a pruned fuzzy neural network, wich favours the compactness of the model. The performance of the proposed approach is evaluated through the usage of databases related to the Optical Interconnection Network. Finally, binary patterns classification tests for the identification of temporal distribution (asynchronous or client–server) were performed and compared with state-of-the-art fuzzy neural-based and traditional machine learning approaches. Results demonstrated that the proposed model is an efficient tool for these challenging classification tasks.

AB - Traditionally, fuzzy neural networks have parametric clustering methods based on equally spaced membership functions to fuzzify inputs of the model. In this sense, it produces an excessive number calculations for the parameters’ definition of the network architecture, which may be a problem especially for real-time large-scale tasks. Therefore, this paper proposes a new model that uses a non-parametric technique for the fuzzification process. The proposed model uses an autonomous data density approach in a pruned fuzzy neural network, wich favours the compactness of the model. The performance of the proposed approach is evaluated through the usage of databases related to the Optical Interconnection Network. Finally, binary patterns classification tests for the identification of temporal distribution (asynchronous or client–server) were performed and compared with state-of-the-art fuzzy neural-based and traditional machine learning approaches. Results demonstrated that the proposed model is an efficient tool for these challenging classification tasks.

KW - Autonomous data density

KW - Fuzzy neural networks

KW - Optical interconnection network

U2 - 10.1007/s12530-020-09336-3

DO - 10.1007/s12530-020-09336-3

M3 - Journal article

AN - SCOPUS:85084204599

VL - 12

SP - 899

EP - 911

JO - Evolving Systems

JF - Evolving Systems

SN - 1868-6478

IS - 4

ER -