A fuzzy neural tree based on likelihood

Ozer Ciftcioglu; Michael S. Bittermann

doi:10.1109/FUZZ-IEEE.2015.7337971

A fuzzy neural tree based on likelihood

Ozer Ciftcioglu
, Michael S. Bittermann

Delft University of Technology
Maltepe University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

A novel type of fuzzy neural system is presented. It involves the neural tree concept and is termed as fuzzy neural tree (FNT). Each tree node uses a Gaussian as a fuzzy membership function so that the approach uniquely is in align with both the probabilistic and possibilistic interpretations of fuzzy membership, thereby presenting a novel type of network. The tree is structured by the domain knowledge and parameterized by likelihood. The FNT is described in detail pointing out its various potential utilizations, in which complex modeling and multi-objective optimization are demanded. One of such utilizations concerns design. This is exemplified and its effectiveness is demonstrated by computer experiments in the realm of Architectural design.

Original language	English
Title of host publication	FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems
Editors	Adnan Yazici, Nikhil R. Pal, Uzay Kaymak, Trevor Martin, Hisao Ishibuchi, Chin-Teng Lin, Joao M. C. Sousa, Bulent Tutmez
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467374286
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2015.7337971
Publication status	Published - 25 Nov 2015
Externally published	Yes
Event	2015 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey Duration: 2 Aug 2015 → 5 Aug 2015

Publication series

Name	IEEE International Conference on Fuzzy Systems
Volume	2015-November
ISSN (Print)	1098-7584

Conference

Conference	2015 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015
Country/Territory	Turkey
City	Istanbul
Period	2/08/15 → 5/08/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

Fuzzy logic
evolutionary computation
knowledge modeling
likelihood
neural tree
probability possibility

Access to Document

10.1109/FUZZ-IEEE.2015.7337971

Cite this

Ciftcioglu, O., & Bittermann, M. S. (2015). A fuzzy neural tree based on likelihood. In A. Yazici, N. R. Pal, U. Kaymak, T. Martin, H. Ishibuchi, C.-T. Lin, J. M. C. Sousa, & B. Tutmez (Eds.), FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems Article 7337971 (IEEE International Conference on Fuzzy Systems; Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2015.7337971

Ciftcioglu, Ozer ; Bittermann, Michael S. / A fuzzy neural tree based on likelihood. FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems. editor / Adnan Yazici ; Nikhil R. Pal ; Uzay Kaymak ; Trevor Martin ; Hisao Ishibuchi ; Chin-Teng Lin ; Joao M. C. Sousa ; Bulent Tutmez. Institute of Electrical and Electronics Engineers Inc., 2015. (IEEE International Conference on Fuzzy Systems).

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abstract = "A novel type of fuzzy neural system is presented. It involves the neural tree concept and is termed as fuzzy neural tree (FNT). Each tree node uses a Gaussian as a fuzzy membership function so that the approach uniquely is in align with both the probabilistic and possibilistic interpretations of fuzzy membership, thereby presenting a novel type of network. The tree is structured by the domain knowledge and parameterized by likelihood. The FNT is described in detail pointing out its various potential utilizations, in which complex modeling and multi-objective optimization are demanded. One of such utilizations concerns design. This is exemplified and its effectiveness is demonstrated by computer experiments in the realm of Architectural design.",

keywords = "Fuzzy logic, evolutionary computation, knowledge modeling, likelihood, neural tree, probability possibility",

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Ciftcioglu, O & Bittermann, MS 2015, A fuzzy neural tree based on likelihood. in A Yazici, NR Pal, U Kaymak, T Martin, H Ishibuchi, C-T Lin, JMC Sousa & B Tutmez (eds), FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems., 7337971, IEEE International Conference on Fuzzy Systems, vol. 2015-November, Institute of Electrical and Electronics Engineers Inc., 2015 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015, Istanbul, Turkey, 2/08/15. https://doi.org/10.1109/FUZZ-IEEE.2015.7337971

A fuzzy neural tree based on likelihood. / Ciftcioglu, Ozer; Bittermann, Michael S.
FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems. ed. / Adnan Yazici; Nikhil R. Pal; Uzay Kaymak; Trevor Martin; Hisao Ishibuchi; Chin-Teng Lin; Joao M. C. Sousa; Bulent Tutmez. Institute of Electrical and Electronics Engineers Inc., 2015. 7337971 (IEEE International Conference on Fuzzy Systems; Vol. 2015-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - A novel type of fuzzy neural system is presented. It involves the neural tree concept and is termed as fuzzy neural tree (FNT). Each tree node uses a Gaussian as a fuzzy membership function so that the approach uniquely is in align with both the probabilistic and possibilistic interpretations of fuzzy membership, thereby presenting a novel type of network. The tree is structured by the domain knowledge and parameterized by likelihood. The FNT is described in detail pointing out its various potential utilizations, in which complex modeling and multi-objective optimization are demanded. One of such utilizations concerns design. This is exemplified and its effectiveness is demonstrated by computer experiments in the realm of Architectural design.

AB - A novel type of fuzzy neural system is presented. It involves the neural tree concept and is termed as fuzzy neural tree (FNT). Each tree node uses a Gaussian as a fuzzy membership function so that the approach uniquely is in align with both the probabilistic and possibilistic interpretations of fuzzy membership, thereby presenting a novel type of network. The tree is structured by the domain knowledge and parameterized by likelihood. The FNT is described in detail pointing out its various potential utilizations, in which complex modeling and multi-objective optimization are demanded. One of such utilizations concerns design. This is exemplified and its effectiveness is demonstrated by computer experiments in the realm of Architectural design.

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KW - evolutionary computation

KW - knowledge modeling

KW - likelihood

KW - neural tree

KW - probability possibility

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A2 - Lin, Chin-Teng

A2 - Sousa, Joao M. C.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Ciftcioglu O, Bittermann MS. A fuzzy neural tree based on likelihood. In Yazici A, Pal NR, Kaymak U, Martin T, Ishibuchi H, Lin CT, Sousa JMC, Tutmez B, editors, FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems. Institute of Electrical and Electronics Engineers Inc. 2015. 7337971. (IEEE International Conference on Fuzzy Systems). doi: 10.1109/FUZZ-IEEE.2015.7337971

A fuzzy neural tree based on likelihood

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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