Thematische Bibliographien / FUZZY SIMILARITY

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „FUZZY SIMILARITY“

Autor: Grafiati
Veröffentlicht am 11. September 2023

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Zeitschriftenartikel zum Thema "FUZZY SIMILARITY"

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BEG, ISMAT, und SAMINA ASHRAF. „FUZZY SIMILARITY AND MEASURE OF SIMILARITY WITH LUKASIEWICZ IMPLICATOR“. New Mathematics and Natural Computation 04, Nr. 02 (Juli 2008): 191–206. http://dx.doi.org/10.1142/s1793005708000994.

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Given a universe of discourse X. A fuzzy similarity mapping ST,Inc : F(X) × F(X) → F(X) is defined, where F(X) denotes the set of all fuzzy subsets of X. Mapping ST,Inc maps two fuzzy sets A and B to a fuzzy set ST,Inc(A,B) in X called their fuzzy set of similarity. A measure of similarity between A and B is then obtained by applying the composite of fuzzy measure and fuzzy similarity mapping on the pair (A,B). Several properties of fuzzy set of similarity and the measure of fuzzy similarity are obtained within the framework of Lukasiewicz fuzzy implicator and its respective t-norm and t-conorm. Many examples of measure of similarity are also constructed.
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Phong, Pham Hong, und Vu Thi Hue. „On Integration Linguistic Factors to Fuzzy Similarity Measures and Intuitionistic Fuzzy Similarity Measures“. International Journal of Synthetic Emotions 10, Nr. 1 (Januar 2019): 1–37. http://dx.doi.org/10.4018/ijse.2019010101.

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The article is concerned with integrating linguistic elements into fuzzy similarity measures and intuitionistic fuzzy similarity measure. Some new concepts are proposed: a fuzzy linguistic value (FLv), a fuzzy linguistic vector (FLV), an intuitionistic fuzzy linguistic vector (ILV) and similarity measures. The proposed measures are used to build classification algorithms. As predicted theoretically, experiments show that with the same type of similarity measures, the linguistic-aggregated similarity measures produce better results in classification problems.
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BEG, ISMAT, und SAMINA ASHRAF. „FUZZY INCLUSION AND FUZZY SIMILARITY WITH GÖDEL FUZZY IMPLICATOR“. New Mathematics and Natural Computation 05, Nr. 03 (November 2009): 617–33. http://dx.doi.org/10.1142/s1793005709001489.

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Fuzzy inclusion and fuzzy similarity are introduced as mappings which produce fuzzy sets constructed with the help of Gödel implicator. The properties of resulting fuzzy sets of inclusion and similarity are studied in detail. Some axiomatic characteristics for being fuzzy orders and fuzzy equivalence relations are also included.
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Soylu, Gültekin. „Similarity-based fuzzy limits“. Fuzzy Sets and Systems 159, Nr. 24 (Dezember 2008): 3380–87. http://dx.doi.org/10.1016/j.fss.2008.03.026.

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Calvo, Tomasa. „On fuzzy similarity relations“. Fuzzy Sets and Systems 47, Nr. 1 (April 1992): 121–23. http://dx.doi.org/10.1016/0165-0114(92)90069-g.

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Liu, Xiao Jing, Wei Feng Du und Xiao Min. „Fuzzy Attribute Reduction Based on Fuzzy Similarity“. Applied Mechanics and Materials 533 (Februar 2014): 237–41. http://dx.doi.org/10.4028/www.scientific.net/amm.533.237.

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The measure of the significance of the attribute and attribute reduction is one of the core content of rough set theory. The classical rough set model based on equivalence relation, suitable for dealing with discrete-valued attributes. Fuzzy-rough set theory, integrating fuzzy set and rough set theory together, extending equivalence relation to fuzzy relation, can deal with fuzzy-valued attributes. By analyzing three problems of FRAR which is a fuzzy decision table attribute reduction algorithm having extensive use, this paper proposes a new reduction algorithm which has better overcome the problem, can handle larger fuzzy decision table. Experimental results show that our reduction algorithm is much quicker than the FRAR algorithm.
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Ovchinnikov, Sergei. „Similarity relations, fuzzy partitions, and fuzzy orderings“. Fuzzy Sets and Systems 40, Nr. 1 (März 1991): 107–26. http://dx.doi.org/10.1016/0165-0114(91)90048-u.

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Wang, De-Gang, Yan-Ping Meng und Hong-Xing Li. „A fuzzy similarity inference method for fuzzy reasoning“. Computers & Mathematics with Applications 56, Nr. 10 (November 2008): 2445–54. http://dx.doi.org/10.1016/j.camwa.2008.03.054.

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Gora, Pawan. „Intuitionistic Fuzzy Modulus Similarity Measure“. International Journal of Decision Support System Technology 15, Nr. 1 (01.01.2023): 1–22. http://dx.doi.org/10.4018/ijdsst.315757.

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The concept of intuitionistic fuzzy sets (IFSs) is an expected explanation for finding the appropriate information. It originated from concept of fuzzy set (FS) theory, which extends the classical conception of a fuzzy set. This paper examines a number of widely employed similarity measures then proposes an IFSs modulus similarity measure and a weight similarity measure. Initially, the authors have discussed numerous existing similarity measures, some of which are unable to justify the axioms of being a similarity measure. Furthermore, some numerical examples are presented to compare the existing similarity measures with the proposed similarity measure. The proposed similarity measure is a practical and effective method for determining the qualitative similarity between IFSs, which do not have any paradoxical nature. In addition, the proposed similarity measure has been demonstrated practically in pattern recognition and medical diagnosis problem. Suggestions for future research comprise the conclusions of the paper.
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Ghosh, Debdas, und Debjani Chakraborty. „On Similarity of Fuzzy Triangles“. International Journal of Fuzzy Logic Systems 3, Nr. 4 (31.10.2013): 1–15. http://dx.doi.org/10.5121/ijfls.2013.3401.

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Dissertationen zum Thema "FUZZY SIMILARITY"

1

Tolt, Gustav. „Fuzzy similarity-based image processing /“. Örebro : Örebro universitetsbibliotek, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-97.

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Chandran, Gautam David. „The development of a fuzzy semantic sentence similarity measure“. Thesis, Manchester Metropolitan University, 2013. http://e-space.mmu.ac.uk/617190/.

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A problem in the field of semantic sentence similarity is the inability of sentence similarity measures to accurately represent the effect perception based (fuzzy) words, which are commonly used in natural language, have on sentence similarity. This research project developed a new sentence similarity measure to solve this problem. The new measure, Fuzzy Algorithm for Similarity Testing (FAST) is a novel ontology-based similarity measure that uses concepts of fuzzy and computing with words to allow for the accurate representation of fuzzy based words. Through human experimentation fuzzy sets were created for six categories of words based on their levels of association with particular concepts. These fuzzy sets were then defuzzified and the results used to create new ontological relations between the fuzzy words contained within them and from that a new fuzzy ontology was created. Using these relationships allows for the creation of a new ontology-based fuzzy semantic text similarity algorithm that is able to show the effect of fuzzy words on computing sentence similarity as well as the effect that fuzzy words have on non-fuzzy words within a sentence. In order to evaluate FAST, two new test datasets were created through the use of questionnaire based human experimentation. This involved the generation of a robust methodology for creating usable fuzzy datasets (including an automated method that was used to create one of the two fuzzy datasets). FAST was evaluated through experiments conducted using the new fuzzy datasets. The results of the evaluation showed that there was an improved level of correlation between FAST and human test results over two existing sentence similarity measures demonstrating its success in representing the similarity between pairs of sentences containing fuzzy words.
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Bashon, Yasmina M. „Contributions to fuzzy object comparison and applications. Similarity measures for fuzzy and heterogeneous data and their applications“. Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6305.

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This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.
Libyan Embassy
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Bashon, Yasmina Massoud. „Contributions to fuzzy object comparison and applications : similarity measures for fuzzy and heterogeneous data and their applications“. Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6305.

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This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.
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IONESCU, MIRCEA MARIAN. „ADAPTIVE MEASURES OF SIMILARITY - FUZZY HAMMING DISTANCE - AND ITS APPLICATIONS TO PATTERN RECOGNITION PROBLEMS“. University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163708478.

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McCulloch, Josie C. „Novel methods of measuring the similarity and distance between complex fuzzy sets“. Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33401/.

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This thesis develops measures that enable comparisons of subjective information that is represented through fuzzy sets. Many applications rely on information that is subjective and imprecise due to varying contexts and so fuzzy sets were developed as a method of modelling uncertain data. However, making relative comparisons between data-driven fuzzy sets can be challenging. For example, when data sets are ambiguous or contradictory, then the fuzzy set models often become non-normal or non-convex, making them difficult to compare. This thesis presents methods of comparing data that may be represented by such (complex) non-normal or non-convex fuzzy sets. The developed approaches for calculating relative comparisons also enable fusing methods of measuring similarity and distance between fuzzy sets. By using multiple methods, more meaningful comparisons of fuzzy sets are possible. Whereas if only a single type of measure is used, ambiguous results are more likely to occur. This thesis provides a series of advances around the measuring of similarity and distance. Based on them, novel applications are possible, such as personalised and crowd-driven product recommendations. To demonstrate the value of the proposed methods, a recommendation system is developed that enables a person to describe their desired product in relation to one or more other known products. Relative comparisons are then used to find and recommend something that matches a person's subjective preferences. Demonstrations illustrate that the proposed method is useful for comparing complex, non-normal and non-convex fuzzy sets. In addition, the recommendation system is effective at using this approach to find products that match a given query.
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Wagholikar, Amol S., und N/A. „Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning“. Griffith University. School of Information and Communication Technology, 2007. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20071214.152324.

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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.
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Wagholikar, Amol S. „Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning“. Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365403.

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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Full Text
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Onescu, Mircea Marian. „Adaptive measures of similarity - fuzzy hamming distance - and its applications to pattern recognition problems“. Cincinnati, Ohio : University of Cincinnati, 2006. http://www.ohiolink.edu/etd/view.cgi?acc%5Fnum=ucin1163708478.

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Thesis (Ph. D.)--University of Cincinnati, 2006..
Title from electronic thesis title page (viewed Jan.27, 2007). Includes abstract. Keywords: Fuzzy Hamming Distance, artificial intelligence, fuzzy, image retrieval system Includes bibliographical references.
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Lo, Yi-Chen. „Detection of gas/odor based on quartz crystal microbalance sensors and fuzzy similarity measure“. To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Bücher zum Thema "FUZZY SIMILARITY"

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Luukka, Pasi. Similarity measure based classification. Lappeenranta: Lappeenranta University of Technology, 2005.

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Cross, Valerie V., und Thomas A. Sudkamp. Similarity and Compatibility in Fuzzy Set Theory. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1793-5.

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A, Sudkamp Thomas, Hrsg. Similarity and compatibility in fuzzy set theory: Assessment and applications. Heidelberg: Physica-Verlag, 2002.

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Kreinovich, Vladik. Strongly transitive fuzzy relations: A more adequate way to describe similarity. [Washington, DC?: National Aeronautics and Space Administration, 1992.

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Sudkamp, Thomas A., und Valerie V. Cross. Similarity and Compatibility in Fuzzy Set Theory: Assessment and Applications. Physica-Verlag, 2013.

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Similarity and Compatibility in Fuzzy Set Theory Studies in Fuzziness and Soft Computing. Physica-Verlag HD, 2010.

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Beaman, Lori G. Alternative Narratives and Getting to Deep Equality. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803485.003.0003.

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This chapter explores three related themes: similarity, cooperation, and contaminated diversity. Each highlights the messy and complex nature of social life and each adds an analytical touchstone for understanding the elements of deep equality. The ability to recognize similarity and the experience of contaminated diversity are two important conditions under which deep equality emerges. By examining the ways that the everyday world does not correspond to categorical positioning around diversity and identity, contaminated diversity can be seen to act as an antidote to purity, while similarity undercuts identity rigidity, and both together render the boundaries of Us and Them fuzzy, sometimes indiscernible, and sometimes laughably irrelevant. This chapter discusses why there is a disproportionate emphasis on conflict and difference in public discourse and scholarship. It draws on a body of research from biology, mathematics, and psychology to examine the notion of competition, and the important counter-narrative of cooperation.
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Buchteile zum Thema "FUZZY SIMILARITY"

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Petry, Frederick E. „Similarity-Based Models“. In Fuzzy Databases, 63–102. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1319-9_3.

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Cross, Valerie, und Valeria Mokrenko. „Fuzzy Set Similarity Between Fuzzy Words“. In Advances in Intelligent Systems and Computing, 214–23. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21920-8_20.

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N. Mordeson, John, Kiran R. Bhutani und Azriel Rosenfeld. „Membership Functions From Similarity Relations“. In Fuzzy Group Theory, 267–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/10936443_10.

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Cross, Valerie V., und Thomas A. Sudkamp. „Fuzzy-Valued Similarity Measures“. In Similarity and Compatibility in Fuzzy Set Theory, 139–42. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1793-5_10.

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Clara, Narcís. „Generalized Fuzzy Similarity Indexes“. In Neural Nets, 163–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731177_24.

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Gerla, Giangiacomo, und Maria I. Sessa. „Similarity in Logic Programming“. In Fuzzy Logic and Soft Computing, 19–31. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5261-1_2.

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Smithson, Michael. „A Class of Fuzzy Featural Models of Similarity Judgments“. In Fuzzy Logic, 377–84. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_35.

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Jović, Mladen, Yutaka Hatakeyama, Fangyan Dong und Kaoru Hirota. „Image Retrieval Based on Similarity Score Fusion from Feature Similarity Ranking Lists“. In Fuzzy Systems and Knowledge Discovery, 461–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_54.

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Thiele, Helmut. „On Similarity-Based Fuzzy Clusterings“. In Applied Logic Series, 289–99. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-1652-9_19.

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Montevechi, J. A. B., G. L. Torres, P. E. Miyagi und M. R. P. Barretto. „Fuzzy logic for similarity analysis“. In Balanced Automation Systems, 171–78. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-0-387-34910-7_16.

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Konferenzberichte zum Thema "FUZZY SIMILARITY"

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Cross, Valerie, Valeria Mokrenko, Keeley Crockett und Naeemeh Adel. „Using Fuzzy Set Similarity in Sentence Similarity Measures“. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177836.

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Adel, Naeemeh, Keeley Crockett, Joao P. Carvalho und Valerie Cross. „Fuzzy Influence in Fuzzy Semantic Similarity Measures“. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494535.

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Baccour, Leila, Adel M. Alimi und Robert I. John. „Relationship between intuitionistic fuzzy similarity measures“. In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007518.

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Coletti, Giulianella, und Bernadette Bouchon-Meunier. „Fuzzy similarity measures and measurement theory“. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858793.

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Adel, Naeemeh, Keeley Crockett, Alan Crispin, David Chandran und Joao P. Carvalho. „FUSE (Fuzzy Similarity Measure) - A measure for determining fuzzy short text similarity using Interval Type-2 fuzzy sets“. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491641.

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Hajighasemi, S. „A fuzzy similarity measure for generalized fuzzy numbers“. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7337851.

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Niyigena, Leoncie, Pasi Luukka und Mikael Collan. „Supplier evaluation with fuzzy similarity based fuzzy TOPSIS with new fuzzy similarity measure“. In 2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2012. http://dx.doi.org/10.1109/cinti.2012.6496767.

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Baccour, Leila, und Adel M. Alimi. „Applications and comparisons of fuzzy similarity measures“. In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584276.

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Chandran, David, Keeley Crockett, David Mclean und Zuhair Bandar. „FAST: A fuzzy semantic sentence similarity measure“. In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622344.

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Guevara, Jorge, Roberto Hirata und Stephane Canu. „Cross product kernels for fuzzy set similarity“. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015459.

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Berichte der Organisationen zum Thema "FUZZY SIMILARITY"

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Zwick, Rami, Edward Carlstein und David Budescu. Measures of Similarity between Fuzzy Concepts: A Comparative Analysis. Fort Belvoir, VA: Defense Technical Information Center, Dezember 1987. http://dx.doi.org/10.21236/ada189430.

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2

Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER und Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.

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In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of inference rules that will make it possible. On the basis of a diagnosis such as the Energy Performance Certificate, it will help establishing a list of priority actions. The dynamics that drive this project permit to decrease the subjectivity of a human decisions making scheme. While simulation generates digital technical data, interpretation requires the translation of this data into natural language. The purpose is to automate the translation of the results to provide advice and facilitate decision-making. In medicine, the diagnostic phase is a process by which a disease is identified by its symptoms. Similarly, the idea of the process is to target the faulty elements potentially responsible for poor performance and to propose remedial solutions. The system is based on the development of fuzzy logic rules [2],[3]. This choice was made to be able to manipulate notions of membership with truth levels between 0 and 1, and to deliver messages in a linguistic form, understandable by non-specialist users. For example, if performance is low and parameter x is unfavourable, the algorithm can gives an incentive to improve the parameter such as: "you COULD, SHOULD or MUST change parameter x". Regarding energy performance analysis, the following domains are addressed: heating, domestic hot water, cooling, lighting. Regarding the parameters, the analysis covers the following topics: Characteristics of the building envelope. and of the technical installations (heat production-distribution, ventilation system, electric lighting, etc.). This paper describes the methodology used, lists the fields studied and outlines the expected outcomes of the project.
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