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Intuitionistic neuro-fuzzy network with evolutionary adaptation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F17%3A39910602" target="_blank" >RIV/00216275:25410/17:39910602 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/s12530-016-9157-5" target="_blank" >http://dx.doi.org/10.1007/s12530-016-9157-5</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s12530-016-9157-5" target="_blank" >10.1007/s12530-016-9157-5</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Intuitionistic neuro-fuzzy network with evolutionary adaptation

  • Original language description

    Intuitionistic fuzzy inference systems (IFISs) incorporate imprecision in the construction of membership functions present in fuzzy inference systems. In this paper we design intuitionistic neuro-fuzzy networks to adapt the antecedent and consequent parameters of IFISs. We also propose a mean of maximum defuzzification method for a class of Takagi-Sugeno IFISs and this method is compared with the basic defuzzification distribution operator. On both real-life credit scoring data and seven benchmark regression datasets we show that the intuitionistic neuro-fuzzy network trained with particle swarm optimization outperforms traditional ANFIS methods (hybrid and backpropagation) and ANFIS trained with evolutionary algorithms (genetic algorithm and particle swarm optimization), respectively. A set of nonparametric tests for multiple datasets is performed to demonstrate statistical differences between the algorithms. In the task of adapting the intuitionistic neuro-fuzzy network, we show that particle swarm optimization provides a higher prediction accuracy compared with traditional algorithms based on gradient descent or least-squares estimation.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA13-10331S" target="_blank" >GA13-10331S: The role of text information in corporate financial distress prediction models – country-specific and industry-specific approaches</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Evolving Systems

  • ISSN

    1868-6478

  • e-ISSN

  • Volume of the periodical

    8

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    13

  • Pages from-to

    35-47

  • UT code for WoS article

    000398452600004

  • EID of the result in the Scopus database

    2-s2.0-85015365604