View in EDS

On training non-uniform fuzzy partitions for function approximation using differential evolution: A study on fuzzy transform and fuzzy projection.

Saved in:
Bibliographic Details
Title: On training non-uniform fuzzy partitions for function approximation using differential evolution: A study on fuzzy transform and fuzzy projection.
Authors: Korkidis, Panagiotis1 (AUTHOR) p.korkidis@uniwa.gr, Dounis, Anastasios1 (AUTHOR) aidounis@uniwa.gr
Source: Information Sciences. Jan2023, Vol. 620, p867-888. 22p.
Subjects: Differential evolution, Partition functions, Evolutionary algorithms, Support vector machines, Fuzzy algorithms
Abstract: This paper focuses on the use of differential evolution to improve the approximation properties of function approximation models based on fuzzy partitions. Two cases are considered: Fuzzy transform and Fuzzy projection, and the design of hybrid evolutionary fuzzy systems, is studied. Even though function approximation techniques based on fuzzy partitions have been well studied, few papers consider the problem of centroid selection of the basis functions. Thus, in most cases uniform fuzzy partitions are considered. By using an evolutionary algorithm a systematic approach on the selection of the partition, is provided. The optimisation problem involves the determination of the model parameters, which in our case are the fuzzy partition's membership functions' locations. The proposed method is tested on the scattered data approximation problem, in a regression sense, that is given a set of sparse data the latent function is approximated. Numerical studies on one and two-dimensional test functions demonstrate that the evolutionary algorithm based fuzzy projection displays high performance in terms of approximation error. Moreover, the proposed approach shows high approximation capabilities with a small number of basis functions. Comparison results, with uniform fuzzy partition models, neural networks and support vector machines, are provided. [ABSTRACT FROM AUTHOR]
Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 160632046
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: On training non-uniform fuzzy partitions for function approximation using differential evolution: A study on fuzzy transform and fuzzy projection.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Korkidis%2C+Panagiotis%22">Korkidis, Panagiotis</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> p.korkidis@uniwa.gr</i><br /><searchLink fieldCode="AR" term="%22Dounis%2C+Anastasios%22">Dounis, Anastasios</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> aidounis@uniwa.gr</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Information+Sciences%22">Information Sciences</searchLink>. Jan2023, Vol. 620, p867-888. 22p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Differential+evolution%22">Differential evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Partition+functions%22">Partition functions</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+algorithms%22">Fuzzy algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper focuses on the use of differential evolution to improve the approximation properties of function approximation models based on fuzzy partitions. Two cases are considered: Fuzzy transform and Fuzzy projection, and the design of hybrid evolutionary fuzzy systems, is studied. Even though function approximation techniques based on fuzzy partitions have been well studied, few papers consider the problem of centroid selection of the basis functions. Thus, in most cases uniform fuzzy partitions are considered. By using an evolutionary algorithm a systematic approach on the selection of the partition, is provided. The optimisation problem involves the determination of the model parameters, which in our case are the fuzzy partition's membership functions' locations. The proposed method is tested on the scattered data approximation problem, in a regression sense, that is given a set of sparse data the latent function is approximated. Numerical studies on one and two-dimensional test functions demonstrate that the evolutionary algorithm based fuzzy projection displays high performance in terms of approximation error. Moreover, the proposed approach shows high approximation capabilities with a small number of basis functions. Comparison results, with uniform fuzzy partition models, neural networks and support vector machines, are provided. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=160632046
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.ins.2022.11.050
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 867
    Subjects:
      – SubjectFull: Differential evolution
        Type: general
      – SubjectFull: Partition functions
        Type: general
      – SubjectFull: Evolutionary algorithms
        Type: general
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Fuzzy algorithms
        Type: general
    Titles:
      – TitleFull: On training non-uniform fuzzy partitions for function approximation using differential evolution: A study on fuzzy transform and fuzzy projection.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Korkidis, Panagiotis
      – PersonEntity:
          Name:
            NameFull: Dounis, Anastasios
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 05
              M: 01
              Text: Jan2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 00200255
          Numbering:
            – Type: volume
              Value: 620
          Titles:
            – TitleFull: Information Sciences
              Type: main
ResultId 1