A learning strategy for fuzzy neural tree is presented that is based on combining the knowledge-driven and data-driven modeling paradigms. The knowledge-driven aspect of the strategy is expressing knowledge via the connection topology of a neural tree. The tree is driven by inputs associated with fuzzy logic. In this type of neural tree, the connection weights are determined in an unsupervised manner. However, the fuzzy logic related parameters are subject to data-driven identification, and they are comparatively few in number. For this reason, a low number of input-output data-pairs suffice to establish the neural representation in the new approach. This makes it suitable for representing evaluation processes of mind that have been difficult to bring into explicit form. An example of this is the evaluation of shape quality in an architectural design, and it is used to verify the effectiveness of the approach by experiment.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Techniques in Big Data Analytics and Decision Making
Subtitle of host publicationProceedings of the INFUS 2019 Conference
EditorsCengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Place of PublicationCham, Switzerland
PublisherSpringer
Pages238-246
Number of pages9
ISBN (Electronic)978-3-030-23756-1
ISBN (Print)978-3-030-23755-4
DOIs
Publication statusPublished - 2020
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey
Duration: 23 Jul 201925 Jul 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1029
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2019
CountryTurkey
CityIstanbul
Period23/07/1925/07/19

    Research areas

  • Architectural design, Evolutionary algorithm, Fuzzy neural tree, Knowledge modeling, Shape evaluation

ID: 68359481