@inproceedings{f4fb1ff3d6d84ca48a0f7179e5f31498,
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",
year = "2020",
doi = "10.1007/978-3-030-23756-1_30",
language = "English",
isbn = "978-3-030-23755-4",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
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",
note = "International Conference on Intelligent and Fuzzy Systems, INFUS 2019 ; Conference date: 23-07-2019 Through 25-07-2019",
}