Relevant bibliographies by topics / Fuzzy Methods

Academic literature on the topic 'Fuzzy Methods'

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Published: 10 March 2023

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Journal articles on the topic "Fuzzy Methods"

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TAKAHAGI, Eiichiro. "Fuzzy Integrals and Fuzzy Reasoning Methods." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 27, no. 1 (2015): 12–19. http://dx.doi.org/10.3156/jsoft.27.1_12.

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Subbotin, Igor Ya, and Michael Gr Voskoglou. "Fuzzy Assessment Methods." Universal Journal of Applied Mathematics 2, no. 9 (December 2014): 305–11. http://dx.doi.org/10.13189/ujam.2014.020902.

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MIZUMOTO, Masaharu. "Fuzzy controls methods." Journal of the Robotics Society of Japan 6, no. 6 (1988): 528–35. http://dx.doi.org/10.7210/jrsj.6.6_528.

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Perrone, G., and S. Noto La Diega. "Fuzzy methods for analysing fuzzy production environment." Robotics and Computer-Integrated Manufacturing 14, no. 5-6 (October 1998): 465–74. http://dx.doi.org/10.1016/s0736-5845(98)00021-0.

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Mizumoto, Masaharu. "Fuzzy controls under various fuzzy reasoning methods." Information Sciences 45, no. 2 (July 1988): 129–51. http://dx.doi.org/10.1016/0020-0255(88)90037-0.

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Kumar, Amit, and Amarpreet Kaur. "Methods for Solving Fully Fuzzy Transportation Problems Based on Classical Transportation Methods." International Journal of Operations Research and Information Systems 2, no. 4 (October 2011): 52–71. http://dx.doi.org/10.4018/joris.2011100104.

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There are several methods, in literature, for finding the fuzzy optimal solution of fully fuzzy transportation problems (transportation problems in which all the parameters are represented by fuzzy numbers). In this paper, the shortcomings of some existing methods are pointed out and to overcome these shortcomings, two new methods (based on fuzzy linear programming formulation and classical transportation methods) are proposed to find the fuzzy optimal solution of unbalanced fuzzy transportation problems by representing all the parameters as trapezoidal fuzzy numbers. The advantages of the proposed methods over existing methods are also discussed. To illustrate the proposed methods a fuzzy transportation problem (FTP) is solved by using the proposed methods and the obtained results are discussed. The proposed methods are easy to understand and to apply for finding the fuzzy optimal solution of fuzzy transportation problems occurring in real life situations.
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WATADA, Junzo. "Methods for Fuzzy Classification." Journal of Japan Society for Fuzzy Theory and Systems 4, no. 1 (1992): 61–73. http://dx.doi.org/10.3156/jfuzzy.4.1_61.

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FURUTA, Hitoshi. "Fuzzy Methods and Design." Journal of the Society of Mechanical Engineers 99, no. 928 (1996): 185–87. http://dx.doi.org/10.1299/jsmemag.99.928_185.

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Işik, Can, and Salwa Ammar. "Fuzzy optimal search methods." Fuzzy Sets and Systems 46, no. 3 (March 1992): 331–37. http://dx.doi.org/10.1016/0165-0114(92)90371-a.

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Hu, Yi-Chung, Ruey-Shun Chen, and Gwo-Hshiung Tzeng. "Discovering fuzzy association rules using fuzzy partition methods." Knowledge-Based Systems 16, no. 3 (April 2003): 137–47. http://dx.doi.org/10.1016/s0950-7051(02)00079-5.

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Dissertations / Theses on the topic "Fuzzy Methods"

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Matthews, Chris, and mikewood@deakin edu au. "Fuzzy concepts and formal methods." Deakin University. School of Management Information Systems, 2001. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20051201.154843.

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It has been recognised that formal methods are useful as a modelling tool in requirements engineering. Specification languages such as Z permit the precise and unambiguous modelling of system properties and behaviour. However some system problems, particularly those drawn from the information systems problem domain, may be difficult to model in crisp or precise terms. It may also be desirable that formal modelling should commence as early as possible, even when our understanding of parts of the problem domain is only approximate. This thesis suggests fuzzy set theory as a possible representation scheme for this imprecision or approximation. A fuzzy logic toolkit that defines the operators, measures and modifiers necessary for the manipulation of fuzzy sets and relations is developed. The toolkit contains a detailed set of laws that demonstrate the properties of the definitions when applied to partial set membership. It also provides a set of laws that establishes an isomorphism between the toolkit notation and that of conventional Z when applied to boolean sets and relations. The thesis also illustrates how the fuzzy logic toolkit can be applied in the problem domains of interest. Several examples are presented and discussed including the representation of imprecise concepts as fuzzy sets and relations, system requirements as a series of linguistically quantified propositions, the modelling of conflict and agreement in terms of fuzzy sets and the partial specification of a fuzzy expert system. The thesis concludes with a consideration of potential areas for future research arising from the work presented here.
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Swartz, Andre Michael. "Methods for designing and optimizing fuzzy controllers." Thesis, Rhodes University, 2000. http://hdl.handle.net/10962/d1005226.

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We start by discussing fuzzy sets and the algebra of fuzzy sets. We consider some properties of fuzzy modeling tools. This is followed by considering the Mamdani and Sugeno models for designing fuzzy controllers. Various methods for using sets of data for desining controllers are discussed. This is followed by a chapter illustrating the use of genetic algorithms in designing and optimizing fuzzy controllers.Finally we look at some previous applications of fuzzy control in telecommunication networks, and illustrate a simple application that was developed as part of the present work.
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Prajitno, Prawito. "Neuro-fuzzy methods in multisensor data fusion." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251258.

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FURUHASHI, Takeshi, and Makoto YASUDA. "Fuzzy Entropy Based Fuzzy c-Means Clustering with Deterministic and Simulated Annealing Methods." Institute of Electronics, Information and Communication Engineers, 2009. http://hdl.handle.net/2237/15060.

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Wang, Yanfei. "Fuzzy methods for analysis of microarrays and networks." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48175/1/Yanfei_Wang_Thesis.pdf.

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Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.
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Abd, Rahim Noor Hafhizah. "Comparing and compressing fuzzy concepts : methods and application." Thesis, University of Bristol, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.682484.

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In recent years, the volume of data has risen so rapidly due to the Internet and World Wide Web development. This phenomenon called information overload or digital obesity has caused data explosion and may lead to storage problems in the future. Many forms of data are stored and transmitted via internet including textual data. Textual data, which is usually in unstructured form can be processed or mined to yield useful information. In order to represent that, we need to know the underlying concepts. The most suitable approach to model the concepts is to design an ontology. Formal Concept Analysis (FCA) is complementary to the ontology approach, and provides a hierarchical structure of the concepts. However, an ontology is a fixed structure which does not change; in contrast, data is typically updated from day to day. The focus of this research is quantifying the changes in the content and structure of these concept hierarchies. it is beneficial if we quantify the changes. There are two types of measurements. The first measures the changes between two lattices which have identical sets of objects, but disjoint sets of attributes. We pair the overlapped concepts and compute the cost to transform each concept to its counterpart. We adapt the Levenstein distance to measure the changes. The second is Support-based Distance measurement, where we quantify the change in two lattices which have different sets of objects but the same set of attributes. We compute the support (or relative cardinality) for each concept's extension. Nowadays, online shopping becomes more common, and many customers, retailers, and manufacturers give attention to the product reviews. Because of that, we apply both measurements to an illustrative application using product review datasets. We monitor the differences between positive and negative sentiment orientations based on a product over fixed period of time using Edit Distance measurement. Additionally, we track the changes between lattices which represent the sentiment orientation on a product in two different time periods using Support-based Distance measurement. The phenomenon of information overload leads to problems using FCA, as it can be difficult to read the lattices and very costly to compute them. These large datasets are often high-dimensional datasets. We enhance an approach to select the important dimensions using Principal Component Analysis (PCA) through the Singular Value Decomposition (SVD) method, so that FCA computation becomes more tractable.
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Siddique, Muhammad. "Fuzzy decision making using max-min and MMR methods." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3042.

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Fuzzy logic is based on the theory of fuzzy sets, where an object’s membership of a set is gradual rather than just member or not a member. Fuzzy logic uses the whole interval of real numbers between zero (False) and one (True) to develop a logic as a basis for rules of inference. Particularly the fuzzified version of the modus ponens rule of inference enables computers to make decisions using fuzzy reasoning rather than exact. We study decision making problem under uncertainty. we analyze Max-Min method and Minimization of regret method originally developed by Savage and further developed by Yager. We generalize The MMR method by creating the parameterized family of minimum regret methods by using the ordered weighted averaging OWA operators.
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Nasser, Sara. "Fuzzy methods for meta-genome sequence classification and assembly." abstract and full text PDF (free order & download UNR users only), 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3307706.

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Eliason, Ryan Lee. "Application of Convex Methods to Identification of Fuzzy Subpopulations." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2242.

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In large observational studies, data are often highly multivariate with many discrete and continuous variables measured on each observational unit. One often derives subpopulations to facilitate analysis. Traditional approaches suggest modeling such subpopulations with a compilation of interaction effects. However, when many interaction effects define each subpopulation, it becomes easier to model membership in a subpopulation rather than numerous interactions. In many cases, subjects are not complete members of a subpopulation but rather partial members of multiple subpopulations. Grade of Membership scores preserve the integrity of this partial membership. By generalizing an analytic chemistry concept related to chromatography-mass spectrometry, we obtain a method that can identify latent subpopulations and corresponding Grade of Membership scores for each observational unit.
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Kotta, Anwesh. "Condition Monitoring : Using Computational intelligence methods." Master's thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-187100.

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Machine tool components are widely used in many industrial applications. In accordance with their usage, a reliable health monitoring system is necessary to detect defects in these components in order monitor machinery performance and avoid malfunction. Even though several techniques have been reported for fault detection and diagnosis, it is a challenging task to implement a condition monitoring system in real world applications due to their complexity in structure and noisy operating environment. The primary objective of this thesis is to develop novel intelligent algorithms for a reliable fault diagnosis of machine tool components. Another objective is to use Micro Electro Mechanical System (MEMS) sensor and interface it with Raspberry pi hardware for the real time condition monitoring. Primarily knowledge based approach with morphological operators and Fuzzy Inference System is proposed, the e˙ectiveness of this approach lies in the selection of structuring elements(SEs). When this is evaluated with di˙erent classes of bearing fault signals, it is able to detect the fault frequencies e˙ectively. Secondarily, An analytical approach with multi class support machine is proposed, this method has uniqueness of learning on its own with out any prior knowledge, the e˙ectiveness of this method lies on selected features and used kernel for converging. Results have shown that RBF (Radial Bias Function) kernel, which is commonly known as gauss kernel has good performance in identifying faults with less computation time. An idea of prototyping these methods has triggered in using Micro Electro Mechanical System (MEMS) sensor for data acquisition and real time Condition Monitoring. LIS3DH accelerometer sensor is used for the data acquisition of spindle for capturing high frequency fault signals. The measured data is analyzed and compared with the industrial sensor k-shear accelerometer type 8792A.
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Books on the topic "Fuzzy Methods"

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Siegfried, Gottwald, ed. Fuzzy sets, fuzzy logic, fuzzy methods with applications. Chichester: J. Wiley, 1995.

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Statistical methods for fuzzy data. Hoboken, N.J: Wiley, 2010.

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Viertl, Reinhard. Statistical Methods for Fuzzy Data. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470974414.

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Simulating fuzzy systems. Berlin: Springer, 2005.

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1929-, Hwang C. L., ed. Fuzzy mathematical programming: Methods and applications. Berlin: Springer-Verlag, 1992.

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Buckley, James J., and Leonard J. Jowers. Monte Carlo Methods in Fuzzy Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76290-4.

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Keung, Lai Kin, and Wang Shouyang 1958-, eds. Fuzzy portfolio optimization: Theory and methods. Berlin: Springer, 2008.

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J, Jowers Leonard, and SpringerLink (Online service), eds. Monte Carlo Methods in Fuzzy Optimization. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10712-2.

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Book chapters on the topic "Fuzzy Methods"

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Collette, Yann, and Patrick Siarry. "Fuzzy methods." In Multiobjective Optimization, 99–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08883-8_4.

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Czogała, Ernest, and Jacek Łęski. "Unsupervised learning Clustering methods." In Fuzzy and Neuro-Fuzzy Intelligent Systems, 93–127. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1853-6_4.

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Chen, Shu-Jen, and Ching-Lai Hwang. "Fuzzy Ranking Methods." In Lecture Notes in Economics and Mathematical Systems, 101–288. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-46768-4_4.

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Marsala, Christophe. "Fuzzy Partitioning Methods." In Granular Computing, 163–86. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1823-9_8.

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El Alaoui, Mohamed. "Frequently Used Multicriteria Decision-Making Methods." In Fuzzy TOPSIS, 41–64. First edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9781003168416-4-4.

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Dikopoulou, Zoumpolia. "Fuzzy Cognitive Maps." In Fuzzy Management Methods, 27–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81496-0_3.

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Diamond, Phil. "Least Squares Methods in Fuzzy Data Analysis." In Fuzzy Logic, 407–16. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_38.

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Mikut, Ralf, Jens Jäkel, and Lutz Gröll. "Inference Methods for Partially Redundant Rule Bases." In Fuzzy Control, 177–85. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_13.

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Denzler, Alexander. "Introduction." In Fuzzy Management Methods, 3–7. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22978-8_1.

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Denzler, Alexander. "Knowledge." In Fuzzy Management Methods, 11–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22978-8_2.

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Conference papers on the topic "Fuzzy Methods"

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Boffa, Stefania, Carmen De Maio, Antonio Di Nola, Giuseppe Fenza, Anna Rita Ferraioli, and Vincenzo Loia. "Unifying fuzzy concept lattice construction methods." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737689.

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Parker, Jonathon K., Lawrence O. Hall, and James C. Bezdek. "Comparison of scalable fuzzy clustering methods." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6250815.

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Granville, N. W. "Fuzzy analysis of fuzzy images." In IEE Colloquium Intelligent Methods in Healthcare and Medical Applications. IEE, 1998. http://dx.doi.org/10.1049/ic:19981034.

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PERFILIEVA, IRINA. "FUZZY NUMERICAL METHODS." In Proceedings of the 5th International FLINS Conference. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777102_0007.

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Zhu, Xiaodong, Zhiqiu Huang, Shuqun Yang, and Guohua Shen. "Fuzzy Implication Methods in Fuzzy Logic." In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). IEEE, 2007. http://dx.doi.org/10.1109/fskd.2007.327.

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Cross, Valerie V., Valeria Mokrenko, and Shangye Chen. "Comparing Similarity and Distance Based Fuzzy Inferencing Methods." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491584.

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Kupka, Jiri, and Pavel Rusnok. "On regression methods based on linguistic descriptions." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7338040.

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Arias-Duart, Anna, Ferran Pares, Dario Garcia-Gasulla, and Victor Gimenez-Abalos. "Focus! Rating XAI Methods and Finding Biases." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882821.

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Asmuss, Svetlana, and Alexander Sostak. "On spline methods of approximation under L-fuzzy information." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007608.

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Dovzan, Dejan, Vito Logar, and Igor Skrjanc. "Solving the sales prediction problem with fuzzy evolving methods." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6250808.

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Reports on the topic "Fuzzy Methods"

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Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For the correct selection of clusters, a comparative analysis of several methods was performed: arithmetic mean ranks, hierarchical methods followed by dendrogram construction, K- means method, which refers to reference methods, in which the number of groups is specified by the user. The cluster analysis of forestries by twenty analytical grounds was not proved by analysis of variance, so the re-clustering of certain objects was carried out according to the nine most significant analytical features. As a result, the forestry was clustered into four clusters. The conducted cluster analysis with the use of different methods allows us to state that their combination helps to select reasonable groupings, clearly illustrate the clustering procedure and rank the obtained forestry clusters.
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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, June 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.
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Rahimi, Noshad. Developing a Mixed-Methods Method to Model Elderly Health Technology Adoption with Fuzzy Cognitive Map, and its Application in Adoption of Remote Health Monitoring Technologies by Elderly Women. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6395.

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Yusrina, Asri, Ulfah Alifia, Shintia Revina, Rezanti Putri Pramana, and Luhur Bima. Is the Game Worth the Candle? Examining the Effectiveness of Initial Teacher Education in Indonesia. Research on Improving Systems of Education (RISE), August 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/106.

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An impactful teacher education programme equips teachers with knowledge and skills to improve their effectiveness. Empirical findings on the effectiveness of teacher preparation programmes show that the accountability of institutions and teachers should not only be based on the knowledge or skills produced but also on student learning. Our study aims to evaluate the effectiveness of a pre-service teacher education programme in Indonesia, known as Pendidikan Profesi Guru Prajabatan or PPG. PPG is a one-year full-time programme in addition to four years of undergraduate teacher education (Bachelor of Education). PPG graduate teachers pass a selection process and receive a teaching certificate upon completion of the programme. We use mixed methods to understand the differences in the outcome of PPG graduates majoring in primary school teacher education to their counterparts who did not attend PPG. To estimate the impact of PPG, we exploit the combination of rules and events in the selection process which allows us to estimate the impact of PPG on teacher performance using fuzzy regression discontinuity design (RDD). Once we attest to the validity of the fuzzy RDD, we find that PPG has no impact on a teacher’s professional knowledge and student outcomes in numeracy and literacy. We argue that this is due to the ineffective selection mechanism in distinguishing the PPG and the comparison group. We conclude that as an initial teacher training programme, PPG did not improve teacher effectiveness. Despite incorporating best practices from effective teacher training into the programme design, PPG does not appear capable of producing a higher-quality teacher.
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5

Karam, Sofia, Morteza Nagahi, Vidanelage Dayarathna, Junfeng Ma, Raed Jaradat, and Michael Hamilton. Integrating systems thinking skills with multi-criteria decision-making technology to recruit employee candidates. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41026.

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The emergence of modern complex systems is often exacerbated by a proliferation of information and complication of technologies. Because current complex systems challenges can limit an organization's ability to efficiently handle socio-technical systems, it is essential to provide methods and techniques that count on individuals' systems skills. When selecting future employees, companies must constantly refresh their recruitment methods in order to find capable candidates with the required level of systemic skills who are better fit for their organization's requirements and objectives. The purpose of this study is to use systems thinking skills as a supplemental selection tool when recruiting prospective employees. To the best of our knowledge, there is no prior research that studied the use of systems thinking skills for recruiting purposes. The proposed framework offers an established tool to HRM professionals for assessing and screening of prospective employees of an organization based on their level of systems thinking skills while controlling uncertainties of complex decision-making environment with the fuzzy linguistic approach. This framework works as an expert system to find the most appropriate candidate for the organization to enhance the human capital for the organization.
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Osipov, Gennadij Sergeevich. The simplest method of solving a fuzzy linear problem EXCHANGE. КультИнформПресс, 2017. http://dx.doi.org/10.18411/spbcsa-2017-4.

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7

Xu, Li. Fuzzy multiobjective mathematical programming in economic systems analysis: design and method. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.471.

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8

Lavrentieva, Olena O., Ihor O. Arkhypov, Olexander I. Kuchma, and Aleksandr D. Uchitel. Use of simulators together with virtual and augmented reality in the system of welders’ vocational training: past, present, and future. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3748.

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The article discusses the theory and methods of simulation training, its significance in the context of training specialists for areas where the lack of primary qualification is critical. The most widespread hardware and software solutions for the organization welders' simulation training that use VR- and AR- technologies have been analyzed. A review of the technological infrastructure and software tools for the virtual teaching-and-production laboratory of electric welding has been made on the example of the achievements of Fronius, MIMBUS, Seabery. The features of creating a virtual simulation of the welding process using modern equipment based on studies of the behavioral reactions of the welder have been shown. It is found the simulators allow not only training, but also one can build neuro-fuzzy logic and design automated and robotized welding systems. The functioning peculiarities of welding's simulators with AR have been revealed. It is shown they make it possible to ensure the forming basic qualities of a future specialist, such as concentration, accuracy and agility. The psychological and technical aspects of the coaching programs for the training and retraining of qualified welders have been illustrated. The conclusions about the significant advantages of VR- and AR-technologies in comparison with traditional ones have been made. Possible directions of the development of simulation training for welders have been revealed. Among them the AR-technologies have been presented as such that gaining wide popularity as allow to realize the idea of mass training in basic professional skills.
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McClure, Michael A., Yitzhak Spiegel, David M. Bird, R. Salomon, and R. H. C. Curtis. Functional Analysis of Root-Knot Nematode Surface Coat Proteins to Develop Rational Targets for Plantibodies. United States Department of Agriculture, October 2001. http://dx.doi.org/10.32747/2001.7575284.bard.

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The goal of this research was to provide a better understanding of the interface between root-knot nematodes, Meloidogyne spp., and their host in order to develop rational targets for plantibodies and other novel methods of nematode control directed against the nematode surface coat (SC). Specific objectives were: 1. To produce additional monoclonal SC antibodies for use in Objectives 2, 3, and 4 and as candidates for development of plantibodies. 2. To determine the production and distribution of SC proteins during the infection process. 3. To use biochemical and immunological methods to perturbate the root-knot nematode SC in order to identify SC components that will serve as targets for rationally designed plantibodies. 4. To develop SC-mutant nematodes as additional tools for defining the role of the SC during infection. The external cuticular layer of nematodes is the epicuticle. In many nematodes, it is covered by a fuzzy material termed "surface coat" (SC). Since the SC is the outermost layer, it may playa role in the interaction between the nematode and its surroundings during all life stages in soil and during pathogenesis. The SC is composed mainly of proteins, carbohydrates (which can be part of glycoproteins), and lipids. SC proteins and glycoproteins have been labeled and extracted from preparasitic second-stage juveniles and adult females of Meloidogyne and specific antibodies have been raised against surface antigens. Antibodies can be used to gain more information about surface function and to isolate genes encoding for surface antigens. Characterization of surface antigens and their roles in different life-stages may be an important step towards the development of alternative control. Nevertheless, the role of the plant- parasitic nematode's surface in plant-nematode interaction is still not understood. Carbohydrates or carbohydrate-recognition domains (CROs) on the nematode surface may interact with CROs or carbohydrate molecules, on root surfaces or exudates, or be active after the nematode has penetrated into the root. Surface antigens undoubtedly play an important role in interactions with microorganisms that adhere to the nematodes. Polyclonal (PC) and monoclonal (MC) antibodies raised against Meloidogyne javanica, M. incognita and other plant-parasitic nematodes, were used to characterize the surface coat and secreted-excreted products of M. javanica and M. incognita. Some of the MC and PC antibodies raised against M. incognita showed cross-reactivity with the surface coat of M. javanica. Further characterization, in planta, of the epitopes recognized by the antibodies, showed that they were present in the parasitic juvenile stages and that the surface coat is shed during root penetration by the nematode and its migration between root cells. At the molecular level, we have followed two lines of experimentation. The first has been to identify genes encoding surface coat (SC) molecules, and we have isolated and characterized a small family of mucin genes from M. incognita. Our second approach has been to study host genes that respond to the nematode, and in particular, to the SC. Our previous work has identified a large suite of genes expressed in Lycopersicon esculentum giant cells, including the partial cDNA clone DB#131, which encodes a serine/threonine protein kinase. Isolation and predicted translation of the mature cDNA revealed a frame shift mutation in the translated region of nematode sensitive plants. By using primers homologous to conserved region of DB#131 we have identified the orthologues from three (nematode-resistant) Lycopersicon peruvianum strains and found that these plants lacked the mutation.
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Event-Triggered Adaptive Robust Control for Lateral Stability of Steer-by-Wire Vehicles with Abrupt Nonlinear Faults. SAE International, July 2022. http://dx.doi.org/10.4271/2022-01-5056.

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Because autonomous vehicles (AVs) equipped with active front steering have the features of time varying, uncertainties, high rate of fault, and high burden on the in-vehicle networks, this article studies the adaptive robust control problem for improving lateral stability in steer-by-wire (SBW) vehicles in the presence of abrupt nonlinear faults. First, an upper-level robust H∞ controller is designed to obtain the desired front-wheel steering angle for driving both the yaw rate and the sideslip angle to reach their correct values. Takagi-Sugeno (T-S) fuzzy modeling method, which has shown the extraordinary ability in coping with the issue of nonlinear, is applied to deal with the challenge of the changing longitudinal velocity. The output of the upper controller can be calculated by a parallel distributed compensation (PDC) scheme. Then an event-triggered adaptive fault-tolerant lower controller (ET-AFTC) is proposed to drive the whole SBW system driving the desired steering angle offered by the upper controller with fewer communication resources and strong robustness. By employing a backstepping technique, the tracking performance is improved. The dynamic surface control (DSC) approach is used to avoid the problem of repeated differentiations, and Nussbaum function is adopted to overcome the difficulty of unknown nonlinear control gain. Both the stability of the upper and lower controllers can be guaranteed by Lyapunov functions. Finally, the simulations of Matlab/Simulink are given to show that the proposed control strategy is effectively able to deal with the abrupt nonlinear fault via less communication resources and perform better in ensuring the yaw stability of the vehicle.
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