A fuzzy neural tree for the evaluation of shape in an architectural design

Michael S. Bittermann; Ecenur Yavuz; Ozer Ciftcioglu

doi:10.1007/978-3-030-23756-1_30

A fuzzy neural tree for the evaluation of shape in an architectural design

Michael S. Bittermann^*
, Ecenur Yavuz
, Ozer Ciftcioglu

^*Bu çalışma için yazışmadan sorumlu yazar

Mimarlık Bölümü

Istanbul Technical University
Delft University of Technology

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
Editörler	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Yayınlayan	Springer Verlag
Sayfalar	238-246
Sayfa sayısı	9
ISBN (Basılı)	9783030237554
DOI'lar	https://doi.org/10.1007/978-3-030-23756-1_30
Yayın durumu	Yayınlandı - 2020
Etkinlik	International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey Süre: 23 Tem 2019 → 25 Tem 2019

Yayın serisi

Adı	Advances in Intelligent Systems and Computing
Hacim	1029
ISSN (Basılı)	2194-5357
ISSN (Elektronik)	2194-5365

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	International Conference on Intelligent and Fuzzy Systems, INFUS 2019
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	23/07/19 → 25/07/19

Bibliyografik not

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Belgeye Erişim

10.1007/978-3-030-23756-1_30

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Bittermann, M. S., Yavuz, E., & Ciftcioglu, O. (2020). A fuzzy neural tree for the evaluation of shape in an architectural design. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference (pp. 238-246). (Advances in Intelligent Systems and Computing; Hac. 1029). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_30

Bittermann, Michael S. ; Yavuz, Ecenur ; Ciftcioglu, Ozer. / A fuzzy neural tree for the evaluation of shape in an architectural design. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. editör / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A.Cagri Tolga. Springer Verlag, 2020. pp. 238-246 (Advances in Intelligent Systems and Computing).

@inproceedings{491b8d7531b14a93ad254dbb6380c9b6,

title = "A fuzzy neural tree for the evaluation of shape in an architectural design",

abstract = "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.",

keywords = "Architectural design, Evolutionary algorithm, Fuzzy neural tree, Knowledge modeling, Shape evaluation",

author = "Bittermann, \{Michael S.\} and Ecenur Yavuz and Ozer Ciftcioglu",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2019 ; Conference date: 23-07-2019 Through 25-07-2019",

year = "2020",

doi = "10.1007/978-3-030-23756-1\_30",

language = "English",

isbn = "9783030237554",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "238--246",

editor = "Cengiz Kahraman and \{Cevik Onar\}, Sezi and Basar Oztaysi and Sari, \{Irem Ucal\} and Selcuk Cebi and A.Cagri Tolga",

booktitle = "Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference",

address = "Germany",

}

Bittermann, MS, Yavuz, E & Ciftcioglu, O 2020, A fuzzy neural tree for the evaluation of shape in an architectural design. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Advances in Intelligent Systems and Computing, hac. 1029, Springer Verlag, pp. 238-246, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Turkey, 23/07/19. https://doi.org/10.1007/978-3-030-23756-1_30

A fuzzy neural tree for the evaluation of shape in an architectural design. / Bittermann, Michael S.; Yavuz, Ecenur; Ciftcioglu, Ozer.
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A.Cagri Tolga. Springer Verlag, 2020. p. 238-246 (Advances in Intelligent Systems and Computing; Hac. 1029).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - A fuzzy neural tree for the evaluation of shape in an architectural design

AU - Bittermann, Michael S.

AU - Yavuz, Ecenur

AU - Ciftcioglu, Ozer

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Architectural design

KW - Evolutionary algorithm

KW - Fuzzy neural tree

KW - Knowledge modeling

KW - Shape evaluation

UR - https://www.scopus.com/pages/publications/85069503088

U2 - 10.1007/978-3-030-23756-1_30

DO - 10.1007/978-3-030-23756-1_30

M3 - Conference contribution

AN - SCOPUS:85069503088

SN - 9783030237554

T3 - Advances in Intelligent Systems and Computing

SP - 238

EP - 246

BT - Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A.Cagri

PB - Springer Verlag

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2019

Y2 - 23 July 2019 through 25 July 2019

ER -

Bittermann MS, Yavuz E, Ciftcioglu O. A fuzzy neural tree for the evaluation of shape in an architectural design. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC, editörler, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Springer Verlag. 2020. p. 238-246. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23756-1_30

A fuzzy neural tree for the evaluation of shape in an architectural design

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Belgeye Erişim

Diğer Dosyalar ve Linkler

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