Статті в журналах: "Fuzzy modal logics"

1

Mironov, A. M. "Fuzzy Modal Logics." Journal of Mathematical Sciences 128, no. 6 (August 2005): 3461–83. http://dx.doi.org/10.1007/s10958-005-0281-1.

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2

Hájek, Petr. "On fuzzy modal logics." Fuzzy Sets and Systems 161, no. 18 (September 2010): 2389–96. http://dx.doi.org/10.1016/j.fss.2009.11.011.

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3

Mattila, Jorma K. "Modifier Logics Based on Graded Modalities." Journal of Advanced Computational Intelligence and Intelligent Informatics 7, no. 2 (June 20, 2003): 72–78. http://dx.doi.org/10.20965/jaciii.2003.p0072.

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Modifier logics are considered as generalizations of "classical" modal logics. Thus modifier logics are so-called multimodal logics. Multimodality means here that the basic logics are modal logics with graded modalities. The interpretation of modal operators is more general, too. Leibniz’s motivating semantical ideas (see [8], p. 20-21) give justification to these generalizations. Semantics of canonical frames forms the formal semantic base for modifier logics. Several modifier systems are given. A special modifier calculus is combined from some "pure" modifier logics. Creating a topological semantics to this special modifier logic may give a basis to some kind of fuzzy topology. Modifier logics of S4-type modifiers will give a graded topological interior operator systems, and thus we have a link to fuzzy topology.

4

Iashin, Boris Leonidovich. "Non-Classical Logics in Modern Science." Философская мысль, no. 1 (January 2023): 15–25. http://dx.doi.org/10.25136/2409-8728.2023.1.39350.

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Non-classical logicians have significantly expanded the traditional field of using logical methods. The first of them was the three-digit logic of Y. Lukasevich. Next came the three-digit logic of A. Bochvar, the "quantum logics" of G. Reichenbach and P. Detush-Fevrier, infinite-valued, probabilistic and other logics. The possibilities of non-classical logics have become widely used in various branches of scientific knowledge. Polysemantic, fuzzy, intuitionistic, modal, relevant and paranoherent, temporal and other non-classical logics are widely used today in physics, computational mathematics, computer science, linguistics, jurisprudence, ethics and other fields of natural science and socio-humanitarian knowledge. The recently increased interest in non-classical logics is explained, first of all, by the fact that various philosophical, syntactic, semantic and metalogical problems that were previously discussed in the scientific community are being replaced by practical interests. The main source of such interest is their wide application in computer science, artificial intelligence and programming. The logic of causality is used in the interpretation of the concepts of "law of nature", "ontological necessity" and "determinism"; temporal modal logics - for modeling, specification and verification of software systems of logical control; logics with vector semantics, combining the features of fuzzy and para-contradictory logics - in solving problems of dynamic verification of production knowledge bases and expert systems.

5

Blondeel, Marjon, Tommaso Flaminio, Steven Schockaert, Lluís Godo, and Martine De Cock. "On the relationship between fuzzy autoepistemic logic and fuzzy modal logics of belief." Fuzzy Sets and Systems 276 (October 2015): 74–99. http://dx.doi.org/10.1016/j.fss.2015.02.018.

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6

Ming-Sheng, Ying. "On standard models of fuzzy modal logics." Fuzzy Sets and Systems 26, no. 3 (June 1988): 357–63. http://dx.doi.org/10.1016/0165-0114(88)90128-5.

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7

Lano, K. "Intuitionistic modal logic and set theory." Journal of Symbolic Logic 56, no. 2 (June 1991): 497–516. http://dx.doi.org/10.2307/2274696.

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The mathematical treatment of the concepts of vagueness and approximation is of increasing importance in artificial intelligence and related research. The theory of fuzzy sets was created by Zadeh [Z] to allow representation and mathematical manipulation of situations of partial truth, and proceeding from this a large amount of theoretical and applied development of this concept has occurred. The aim of this paper is to develop a natural logic and set theory that is a candidate for the formalisation of the theory of fuzzy sets. In these theories the underlying logic of properties and sets is intuitionistic, but there is a subset of formulae that are ‘crisp’, classical and two-valued, which represent the certain information. Quantum logic or logics weaker than intuitionistic can also be adopted as the basis, as described in [L]. The relationship of this theory to the intensional set theory MZF of [Gd] and the global intuitionistic set theory GIZF of Takeuti and Titani [TT] is also treated.

8

Cerami, Marco, Francesc Esteva, and Àngel García-Cerdaña. "On the relationship between fuzzy description logics and many-valued modal logics." International Journal of Approximate Reasoning 93 (February 2018): 372–94. http://dx.doi.org/10.1016/j.ijar.2017.11.006.

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9

Stankovic, Marko, Miroslav Ciric, and Jelena Ignjatovic. "Simulations and bisimulations for fuzzy multimodal logics over Heyting algebras." Filomat 37, no. 3 (2023): 711–43. http://dx.doi.org/10.2298/fil2303711s.

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In the present paper, we study fuzzy multimodal logics over complete Heyting algebras and Kripke models for these logics. We introduce two types of simulations (forward and backward) and five types of bisimulations (forward, backward, forward-backward, backward-forward and regular) between Kripke models, as well as the corresponding presimulations and prebisimulations, which are simulations and bisimulations with relaxed conditions. For each type of simulations and bisimulations an efficient algorithm has been provided that works as follows: it computes the greatest presimulation/prebisimulation of that type, and then checks whether it meets the additional condition: if it does, then it is also the greatest simulation/ bisimulation of that type, otherwise, there is not any simulation/bisimulation of that type. The algorithms are inspired by algorithms for checking the existence and computing the greatest simulations and bisimulations between fuzzy automata. We also demonstrate the application of these algorithms in the state reduction of Kripke models. We show that forward bisimulation fuzzy equivalences on the Kripke model provide reduced models equivalent to the original model concerning plus-formulas, backward bisimulation fuzzy equivalences provide reduced models equivalent concerning minus-formulas, while regular bisimulation fuzzy equivalences provide reduced models equivalent concerning all modal formulas.

10

Sakai, Hiroshi, and Masahiro Inuiguchi. "Special Issue on Rough Sets and Granular Computing." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 5 (September 20, 2006): 605. http://dx.doi.org/10.20965/jaciii.2006.p0605.

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Rough sets and granular computing, known as new methodologies for computing technology, are now attracting great interest of researchers. This special issue presents 12 articles, and most of them were presented at the second Japanese workshop on Rough Sets held at Kyushu Institute of Technology in Tobata, Kitakyushu, Japan, on August 17-18, 2005. The first article studies the relation between rough set theory and formal concept analysis. These two frameworks are analyzed and connected by using the method of morphism. The second article introduces object-oriented paradigm into rough set theory, and object-oriented rough set models are proposed. Theoretical aspects of these new models are also examined. The third article considers relations between generalized rough sets, topologies and modal logics, and some topological properties of rough sets induced by equivalence relations are presented. The fourth article focuses on a family of polymodal systems, and theoretical aspects of these systems, like the completeness, are investigated. By means of combining polymodal logic concept and rough set theory, a new framework named multi-rough sets is established. The fifth article focuses on the information incompleteness in fuzzy relational models, and a generalized possibility-based fuzzy relational model is proposed. The sixth article presents a developed software EVALPSN (Extended Vector Annotated Logic Program with Strong Negation) and the application of this software to pipeline valve control. The seventh article presents the properties of attribute reduction in variable precision rough set models. Ten kinds of meaningful reducts are newly proposed, and hierarchical relations in these reducts are examined. The eighth article proposes attribute-value reduction for Kansei analysis using information granulation, and illustrative results for some databases in UCI Machine Learning Repository are presented. The ninth article investigates cluster analysis for data with errors tolerance. Two new clustering algorithms, which are based on the entropy regularized fuzzy c-means, are proposed. The tenth article applies binary decision trees to handwritten Japanese Kanji recognition. The consideration to the experimental results of real Kanji data is also presented. The eleventh article applies a rough sets based method to analysing the character of the screen-design in every web site. The obtained character gives us good knowledge to generate a new web site. The last article focuses on rule generation in non-deterministic information systems. For generating minimal certain rules, discernibility functions are introduced. A new algorithm is also proposed for handling every discernibility function. Finally, we would like to acknowledge all the authors for their efforts and contributions. We are very grateful to reviewers for their thorough and on-time reviews, too. We are also grateful to Prof. Toshio Fukuda and Prof. Kaoru Hirota, Editors-in-Chief of JACIII, for inviting us to serve as Guest Editors of this Journal, and to Mr. Uchino and Mr. Ohmori of Fuji Technology Press for their kind assistance in publication of this special issue.

11

Raj, A. Stanley, D. Hudson Oliver, and Y. Srinivas. "Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model." International Journal of Geophysics 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/134834.

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Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms of fuzzy logic, namely, fuzzyC-means clustering, fuzzyK-means clustering, and fuzzy subtractive clustering, were analyzed with the help of fuzzy inference system (FIS) training on synthetic data. Here in this approach, graphical user interface (GUI) was developed with the integration of three algorithms and the input data (AB/2 and apparent resistivity), while importing will process each algorithm and interpret the layer model parameters (true resistivity and depth). A complete overview on the three above said algorithms is presented in the text. It is understood from the results that fuzzy logic subtractive clustering algorithm gives more reliable results and shows efficacy of soft computing tools in the inversion of geoelectrical resistivity data.

12

Escobar-Gómez, Elías Neftalí, Juan José Díaz-Núñez, and León Fernando Taracena-Sanz. "Modelo para el ajuste de pronósticos agregados utilizando lógica difusa." Ingeniería, investigación y tecnología 11, no. 3 (July 1, 2010): 289–302. http://dx.doi.org/10.22201/fi.25940732e.2010.11n3.025.

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13

Giordano, Laura, Valentina Gliozzi, and Daniele Theseider DuprÉ. "A conditional, a fuzzy and a probabilistic interpretation of self-organizing maps." Journal of Logic and Computation 32, no. 2 (January 17, 2022): 178–205. http://dx.doi.org/10.1093/logcom/exab082.

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Abstract In this paper we establish a link between fuzzy and preferential semantics for description logics and self-organizing maps (SOMs), which have been proposed as possible candidates to explain the psychological mechanisms underlying category generalization. In particular, we show that the input/output behavior of a SOM after training can be described by a fuzzy description logic interpretation as well as by a preferential interpretation, based on a concept-wise multipreference semantics, which takes into account preferences with respect to different concepts and has been recently proposed for ranked and for weighted defeasible description logics. Properties of the network can be proven by model checking on the fuzzy or on the preferential interpretation. Starting from the fuzzy interpretation, we also provide a probabilistic account for this neural network model.

14

Liu, Jie, and Lin Lin Cui. "Extending Description Logic SHOIN Based on Cloud Model." Applied Mechanics and Materials 543-547 (March 2014): 3586–89. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3586.

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The current research progress and the existing problems of semantics representation of the semantic Wed are analyzed. In this paper, we present an uncertain description logic C-SHOIN, which is an extension of description logics SHOIN based on cloud model. The syntax and semantics of description logic C-SHOIN are given; as well as the satisfiability of uncertain concepts. Example analysis shows that C-SHOIN is capable as the ontology language extensions to the representation of fuzzy and uncertain knowledge.

15

Sudibyo, Pandu, Yanu Shalahuddin, and Mochtar Yahya. "Single Axis Tracking PV Panel Using Fuzzy Logic Control." JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer 1, no. 1 (January 1, 2021): 1. http://dx.doi.org/10.32503/jtecs.v1i1.646.

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Abstrak – Panel PV(Photovoltaic) merupakan teknologi yang mengubah energi cahaya matahari menjadi energi listrik. Maka dari itu untuk mendapatkan iradiansi maksinal perlu sistem solar tracker sebagai cara untuk optimalisasi penyerapan cahaya matahari. Pada penelitian ini membahas pembuatan model simulink solar tracker menggunakan kontroler fuzzy logic. Arah sinar matahari disensor mengguanakan 2 buah sensor LDR (Light Dependent Resistor) yang selanjutnya menjadi input logika fuzy. Sistem terdiri atas 4 komponen utama yaitu PV Modul ,Mikrokontroler, motor servo, sensor LDR(Light Dependent Resistor) yang selanjutnya menjadi input logika fuzy. Output logika fuzy berupa nilai yang kemudian diumpan ke servo untuk gerakan panel secara Single Axis. Aplikasi Matlab Simulink sebagai compiler dan pembuat permodelan sistem yang nantinya akan diupload ke mikrokontroler. Arah putaran motor servo ditentukan dengan menggunakan kendali logika fuzzy. Hasil pengujian membuktikan rata-rata tegangan panel PV lebih tinggi daripada panel tanpa kendali, dengan nilai rata-rata sebesar 14,35V.

16

JANSSEN, JEROEN, DIRK VERMEIR, STEVEN SCHOCKAERT, and MARTINE DE COCK. "Reducing fuzzy answer set programming to model finding in fuzzy logics." Theory and Practice of Logic Programming 12, no. 6 (June 21, 2011): 811–42. http://dx.doi.org/10.1017/s1471068411000093.

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AbstractIn recent years, answer set programming (ASP) has been extended to deal with multivalued predicates. The resulting formalismsallow for the modeling of continuous problems as elegantly as ASP allows for the modeling of discrete problems, by combining thestable model semantics underlying ASP with fuzzy logics. However, contrary to the case of classical ASP where manyefficient solvers have been constructed, to date there is no efficient fuzzy ASP solver. A well-knowntechnique for classical ASP consists of translating an ASP program P to a propositional theory whose models exactlycorrespond to the answer sets of P. In this paper, we show how this idea can be extended to fuzzy ASP, paving the wayto implement efficient fuzzy ASP solvers that can take advantage of existing fuzzy logic reasoners.

17

Kumar, Vivek, Vinita Rani, Nishant Anand, Nilesh Kumar Patel, Shailja Pandey, Neelesh Kumar Jain, and Dinesh Goyal. "A mathematical taxonomy towards fuzzy based optimized cloud sales services." Journal of Interdisciplinary Mathematics 26, no. 3 (2023): 407–16. http://dx.doi.org/10.47974/jim-1671.

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In the highly working system based on demand service model for end users. The on-demand service model consists of memory storage, hardware management, information processing and various IoT applications using mathematical programming logics with optimized fuzzy model. By using the fuzzy model, the end users get the best optimal cloud services. For providing cloud services in an optimized way the fuzzy logic is playing an important role. The developed algorithm is efficient in terms of cost and time for the cloud service providers. The simulation of our algorithm is done in cloud simulator.

18

Pal’chunov, D. E., and G. E. Yakhyaeva. "Fuzzy Logics and Fuzzy Model Theory." Algebra and Logic 54, no. 1 (March 2015): 74–80. http://dx.doi.org/10.1007/s10469-015-9326-9.

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19

Maksimović, Aleksandar, Adis Puška, Branka Šakić Bobić, and Zoran Grgić. "A model for supporting the decision of plum variety selection based on fuzzy logic." Journal of Central European Agriculture 22, no. 2 (2021): 450–61. http://dx.doi.org/10.5513/jcea01/22.2.2946.

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20

Obijiaku, Callistus Chisom, and Kyungbaek Kim. "Price estimation based on business model pricing strategy and fuzzy logic." Korean Institute of Smart Media 12, no. 1 (February 28, 2023): 54–61. http://dx.doi.org/10.30693/smj.2023.12.1.54.

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Pricing, as one of the most important aspects of a business, should be taken seriously. Whatever affects a company's pricing system tends to affect its profits and losses as wall. Currently, many manufacturing companies fix product prices manually by member of an organization's management team. However, due to the imperfect nature of humans, an extremely low or high price may be fixed, which is detrimental to the company in either case. This paper proposes the developement of a fuzzy-based price expert system (Expert Fuzzy Price (EFP)) for manufacturing companies. This system will be able to recommend appropriate prices for products in manufacturing companies based on four major pricing strategic goals, namely : Product Demand, Price Skimming, Competition Price, and Target population.

21

Perlovsky, Leonid, and Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 4 (October 2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.

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Mind is based on intelligent cognitive processes, which are not limited by language and logic only. The thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures, and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized. Semiotic approach allows for the universal representation of such diagrams. In that approach, logic is a way of synthesis of such structures, which is a small but clearly visible top of the iceberg. The most efforts were traditionally put into logics without paying much attention to the rest of the mechanisms that make the entire thought system working autonomously. Dynamic fuzzy logic is reviewed and its connections with semiotics are established. Dynamic fuzzy logic extends fuzzy logic in the direction of logic-processes, which include processes of fuzzification and defuzzification as parts of logic. The paper reviews basic cognitive mechanisms, including instinctual drives, emotional and conceptual mechanisms, perception, cognition, language, a model of interaction between language and cognition upon the new semiotic models. The model of interacting cognition and language is organized in an approximate hierarchy of mental representations from sensory percepts at the “bottom” to objects, contexts, situations, abstract concepts-representations, and to the most general representations at the “top” of mental hierarchy. Knowledge Instinct and emotions are driving feedbacks for these representations. Interactions of bottom-up and top-down processes in such hierarchical semiotic representation are essential for modeling cognition. Dynamic fuzzy logic is analyzed as a fundamental mechanism of these processes. Future research directions are discussed.

22

Jenarthanan, M. P., A. Ram Prakash, and R. Jeyapaul. "Experimental investigation of machinability characteristics in Al-TiB2 metal matrix composite (MMC) based on the Taguchi method with fuzzy logics." Multidiscipline Modeling in Materials and Structures 12, no. 1 (June 13, 2016): 177–93. http://dx.doi.org/10.1108/mmms-04-2015-0018.

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Purpose – The purpose of this paper is to develop a mathematical model for metal removal rate and surface roughness through Taguchi method and analyse the influence of the individual input machining parameters (cutting speed, feed rate, helix angle, depth of cut and wt% on the responses in milling of aluminium-titanium diboride metal matrix composite (MMC) with solid carbide end mill cutter coated with nano-crystals. Design/methodology/approach – Taguchi OA is used to optimise the material removal rate (MRR) and Surface Roughness by developing a mathematical model. End Milling is used to create slots by combining various input parameters. Five factors, three-level Taguchi method is employed to carry out the experimental investigation. Fuzzy logic is used to find the optimal cutting factors for surface roughness (Ra) and MRR. The factors considered were weight percentage of TiB2, cutting speed, depth of cut and feed rate. The plan for the experiments and analysis was based on the Taguchi L27 orthogonal array with five factors and three levels. MINITAB 17 software is used for regression, S/N ratio and analysis of variance. MATLAB 7.10.0 is used to perform the fuzzy logics systems. Findings – Using fuzzy logics, multi-response performance index is generated, with which the authors can identify the correct combination of input parameters to get higher MRR and lower surface roughness value with the chosen range with 95 per cent confidence intervals. Using such a model, remarkable savings in time and cost can be obtained. Originality/value – Machinability characteristics in Al-TiB2 MMC based on the Taguchi method with fuzzy logic has not been analysed previously.

23

Hájek, Petr, and Petr Cintula. "On theories and models in fuzzy predicate logics." Journal of Symbolic Logic 71, no. 3 (September 2006): 863–80. http://dx.doi.org/10.2178/jsl/1154698581.

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AbstractIn the last few decades many formal systems of fuzzy logics have been developed. Since the main differences between fuzzy and classical logics lie at the propositional level, the fuzzy predicate logics have developed more slowly (compared to the propositional ones). In this text we aim to promote interest in fuzzy predicate logics by contributing to the model theory of fuzzy predicate logics. First, we generalize the completeness theorem, then we use it to get results on conservative extensions of theories and on witnessed models.

24

Poturica, Goran. "Student working time arrangement model based on fuzzy logic." Vojnotehnicki glasnik 46, no. 6 (1998): 209–17. http://dx.doi.org/10.5937/vojtehg9802209p.

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25

Jain, Manisha, Alexandre Madeira, and Manuel A. Martins. "A Fuzzy Modal Logic for Fuzzy Transition Systems." Electronic Notes in Theoretical Computer Science 348 (March 2020): 85–103. http://dx.doi.org/10.1016/j.entcs.2020.02.006.

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26

Wang, Hong, Yu Qiu Liu, and Li Hui Zhou. "Research on an Improved Cellular Automata Model." Applied Mechanics and Materials 160 (March 2012): 109–14. http://dx.doi.org/10.4028/www.scientific.net/amm.160.109.

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We introduce type-2 fuzzy reasoning to models of cellular automata, and combine type-2 fuzzy logic and classic cellular automata model to establish a new model of evolution reasoning, cellular automata model based on type-2 fuzzy logic. The key parts of cellular automata are transition functions and cell states. We fuzzify the transition functions of cellular automata into type-2 fuzzy rules, and cell states into type-2 fuzzy states too. Thus, we establish an improved cellular automata model based on type-2 fuzzy logic.

27

KUVICH, GARY, and LEONID PERLOVSKY. "COGNITIVE MECHANISMS OF THE MIND." New Mathematics and Natural Computation 09, no. 03 (October 3, 2013): 301–23. http://dx.doi.org/10.1142/s1793005713400097.

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Successes of information and cognitive science brought a growing understanding that mind is based on intelligent cognitive processes, which are not limited by language and logic only. A nice overview can be found in the excellent work of Jeff Hawkins "On Intelligence." This view is that thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized and programmed. Semiotic approach allows for the universal representation of such diagrams. In our approach, logic is just a way of synthesis of such structures, which is a small but clearly visible top of the iceberg. However, most of the efforts were traditionally put into logics without paying much attention to the rest of the mechanisms that make the entire thought system working autonomously. Dynamic fuzzy logic is reviewed and its connections with semiotics are established. Dynamic fuzzy logic extends fuzzy logic in the direction of logic-processes, which include processes of fuzzification and defuzzification as parts of logic. This extension of fuzzy logic is inspired by processes in the brain-mind. The paper reviews basic cognitive mechanisms, including instinctual drives, emotional and conceptual mechanisms, perception, cognition, language, a model of interaction between language and cognition upon the new semiotic models. The model of interacting cognition and language is organized in an approximate hierarchy of mental representations from sensory percepts at the "bottom" to objects, contexts, situations, abstract concepts-representations, and to the most general representations at the "top" of mental hierarchy. Knowledge instinct and emotions are driving feedbacks for these representations. Interactions of bottom-up and top-down processes in such hierarchical semiotic representation are essential for modeling cognition. Dynamic fuzzy logic is analyzed as a fundamental mechanism of these processes. In this paper we are trying to formalize cognitive processes of the human mind using approaches above, and provide interfaces that could allow for their practical realization in software and hardware. Future research directions are discussed.

28

Booth, Matthew, and Fabien Paillusson. "A Fuzzy Take on the Logical Issues of Statistical Hypothesis Testing." Philosophies 6, no. 1 (March 15, 2021): 21. http://dx.doi.org/10.3390/philosophies6010021.

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Statistical Hypothesis Testing (SHT) is a class of inference methods whereby one makes use of empirical data to test a hypothesis and often emit a judgment about whether to reject it or not. In this paper, we focus on the logical aspect of this strategy, which is largely independent of the adopted school of thought, at least within the various frequentist approaches. We identify SHT as taking the form of an unsound argument from Modus Tollens in classical logic, and, in order to rescue SHT from this difficulty, we propose that it can instead be grounded in t-norm based fuzzy logics. We reformulate the frequentists’ SHT logic by making use of a fuzzy extension of Modus Tollens to develop a model of truth valuation for its premises. Importantly, we show that it is possible to preserve the soundness of Modus Tollens by exploring the various conventions involved with constructing fuzzy negations and fuzzy implications (namely, the S and R conventions). We find that under the S convention, it is possible to conduct the Modus Tollens inference argument using Zadeh’s compositional extension and any possible t-norm. Under the R convention we find that this is not necessarily the case, but that by mixing R-implication with S-negation we can salvage the product t-norm, for example. In conclusion, we have shown that fuzzy logic is a legitimate framework to discuss and address the difficulties plaguing frequentist interpretations of SHT.

29

Khoi, Phan Bui, and Nguyen Van Toan. "APPLICATION OF HEDGE ALGEBRAS FOR CONTROLLING MECHANISMS OF RELATIVE MANIPULATION." Vietnam Journal of Science and Technology 55, no. 5 (October 20, 2017): 572. http://dx.doi.org/10.15625/2525-2518/55/5/8841.

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This paper presents a method for controlling mechanism of relative manipulation (MRM robot), that based on an algebraic approach to linguistic hedges in fuzzy logic. The proposed model of MRM robot is introduced as two component mechanisms, collaborating to realize technological manipulations. MRM robot has complex structure [1, [2]; therefore, robot system's mathematical equations describing dynamical behaviors are complicated and voluminous [3,[4, 5]. Furthermore, the components affect MRM robot's dynamics that are difficult to determine adequately and exactly. Applying the well-known methods (based on dynamical equations) such as PD/PID, computed torque algorithm...for robot control is difficult, especially with MRM robot. By dint of the human-like inference mechanism, designing controller thanks to fuzzy logic can overcome the mentioned drawbacks [6]. However, the linguistic variables in fuzzy logic are not represented by any physical values; and hence, the comparison between the linguistic variables is unable. Moreover, composition of fuzzy relations, defuzzification use approximation function which can trigger error in data process. Hedge Algebras(HA) gives favorable conditions to restrict fuzzy logic's drawbacks because the linguistic labels in Hedge Algebras are represented by semantic values; and, composition of fuzzy relations and defuzzification are processed by simple interpolation and mapping functions. The obtained results from HA controller are compared to the obtained results from two methods which are presented in [6] (fuzzy controller and computed torque controller). Keywords: mechanism of relative manipulation (MRM robot), hedge algebras.

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Paksoy, Mahmut, Rahmi Guclu, and Saban Cetin. "Semiactive Self-Tuning Fuzzy Logic Control of Full Vehicle Model with MR Damper." Advances in Mechanical Engineering 6 (January 1, 2014): 816813. http://dx.doi.org/10.1155/2014/816813.

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Intelligent controllers are studied for vibration reduction of a vehicle consisting in a semiactive suspension system with a magnetorheological(MR) damper. The vehicle is modeled with seven degrees of freedom as a full vehicle model. The semiactive suspension system consists of a linear spring and an MR damper. MR damper is modeled using Bouc-Wen hysteresis phenomenon and applied to a full vehicle model. Fuzzy Logic based controllers are designed to determine the MR damper voltage. Fuzzy Logic and Self-Tuning Fuzzy Logic controllers are applied to the semiactive suspension system. Results of the system are investigated by simulation studies in MATLAB-Simulink environment. The performance of the semiactive suspension system is analyzed with and without control. Simulation results showed that both Fuzzy Logic and Self-Tuning Fuzzy Logic controllers perform better compared to uncontrolled case. Furthermore, Self-Tuning Fuzzy Logic controller displayed a greater improvement in vibration reduction performance compared to Fuzzy Logic controller.

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Nakamura, Akira, and Jian-Ming Gao. "On a KTB-modal fuzzy logic." Fuzzy Sets and Systems 45, no. 3 (February 1992): 327–34. http://dx.doi.org/10.1016/0165-0114(92)90151-s.

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Fan, Tuan-Fang. "Fuzzy Bisimulation for Gödel Modal Logic." IEEE Transactions on Fuzzy Systems 23, no. 6 (December 2015): 2387–96. http://dx.doi.org/10.1109/tfuzz.2015.2426724.

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Milošević, Teodora, Dragan Pamučar, and Prasenjit Chatterjee. "Model for selecting a route for the transport of hazardous materials using a fuzzy logic system." Vojnotehnicki glasnik 69, no. 2 (2021): 355–90. http://dx.doi.org/10.5937/vojtehg69-29629.

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Introduction/purpose: The paper presents a model for the selection of a route for the transport of hazardous materials using fuzzy logic systems, as a type of artificial intelligence systems. The system presented in the paper is a system for assistance in the decisionmaking process of the traffic service authorities when choosing one of several possible routes on a particular path when transporting hazardous materials. Methods: The route evaluation is performed on the basis of five criteria. Each input variable is represented by three membership functions, and the output variable is defined by five membership functions. All rules in a fuzzy logic system are determined by applying the method of weight premise aggregation (ATPP), which allows the formation of a database based on experience and intuition. Based on the number of input variables and the number of their membership functions, the basic base of 243 rules is defined. Three experts from the Ministry of Defense were interviewed to determine the weighting coefficients of the membership functions, and the values of the coefficients were determined using the Full Consistency Method (FUCOM). Results: A user program which enables the practical application of this model has been created for the developed fuzzy logic system. Conclusion: The user platform was developed in the Matlab 2008b software package.

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Li, Ye, Xiao Liu, Zhenliang Yang, Chao Zhang, Mingchun Song, Zhaolu Zhang, Shiyong Li, and Weiqiang Zhang. "Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic." Scientific Programming 2022 (March 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/2630953.

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The development and distribution of geologically complicated fault structure have the characteristics of uncertainty, randomness, ambiguity, and variability. Therefore, the prediction of complicated fault structures is a typical nonlinear problem. Neither fuzzy logic method nor artificial neural network alone can solve this problem well because the fuzzy method is generally not easy to realize adaptive learning function, and the neural network method is not suitable for describing sedimentary microfacies or geophysical facies. Therefore, taking the marginal subsags in the Jiyang Depression, Eastern China, as a study case, this paper uses the method of combining artificial neural network and fuzzy logic to study geologically complicated fault structure prediction model. This paper expounds on the research status and significance of geologically complicated fault structure prediction model, elaborates the development background, current status, and future challenges of artificial neural networks and fuzzy logic, introduces the method and principle of fuzzy neural network structure and fuzzy logic analysis algorithm, conducts prediction model design and implementation based on fuzzy neural network, proposes the learning algorithm of fuzzy neural network, analyzes the programming realization of fuzzy neural network, constructs complicated fault structure prediction model based on the artificial neural network and fuzzy logic, performs the fuzzy logic system selection of complicated fault structure prediction model, carries out the artificial neural network structure design of complicated fault structure prediction model, compares the prediction effects of the geologically complicated fault structure model based on artificial neural networks and fuzzy logic, and finally discusses the system design and optimization of the prediction model for geologically complicated fault structures. The study results show that the fuzzy neural network fully integrates the advantages of artificial neural network and fuzzy logic system; based on the clear physical background of fuzzy logic system, it effectively integrates powerful knowledge expression ability and fuzzy reasoning ability into the network knowledge structure of neural network, which greatly improves the prediction accuracy of geologically complicated fault structure.

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Wichit, Nattawut, and Anant Choksuriwong. "Multi-sensor Data Fusion Model Based Kalman Filter Using Fuzzy Logic for Human Activity Detection." International Journal of Information and Electronics Engineering 5, no. 6 (2015): 450–53. http://dx.doi.org/10.7763/ijiee.2015.v5.577.

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Shvets, D. V. "Mathematical model for controlling the classification process of crushed iron raw materials using fuzzy logic." Jornal of Kryvyi Rih National University, no. 55 (2022): 156–63. http://dx.doi.org/10.31721/2306-5451-2022-1-55-156-163.

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Frantti, Tapio, and Petri Mähönen. "Fuzzy logic-based forecasting model." Engineering Applications of Artificial Intelligence 14, no. 2 (April 2001): 189–201. http://dx.doi.org/10.1016/s0952-1976(00)00076-2.

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Karim, M. Shahidul, and Rashed Mustafa. "Fuzzy Hybrid Controller Model for Making Decision to Interpret Any Condition." Solid State Phenomena 111 (April 2006): 167–70. http://dx.doi.org/10.4028/www.scientific.net/ssp.111.167.

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The constantly increasing performance/price ratio of microcontrollers means electronic system can replace more and more electromechanical ones. In design, the goal is not to just replace the solution but also to improve it by adding new functionalities. The paper presents a model of industrial controller having possibility of the classical programming controller, with added elements of the fuzzy logic. Here fuzzy logic offers a technical control strategy that uses elements of everyday language. In this application, it is used to design a control strategy that adapts to the need of individual user. It achieves a higher comfort level and reduces energy consumption. Here we have used a fuzzy method which selects the contractions that best meet the specifications, where human knowledge is involved in a decision making process. With a fuzzy-logic software development system, the entire system, which includes conventional code for signal preprocessing as well as the fuzzy logic system, can be implemented on an industry-standard microcontroller. Using fuzzy logic on such a low-cost platform makes this a possible solution with most AC systems. Each home AC has a sensor that measures room temperature and compares it with the temperature set on the dial. The fuzzy logic controller uses a bimetallic switch and compares the set temperature with room temperature.

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Xiao, Jie, Bohdan T. Kulakowski, and Moustafa EI-Gindy. "Prediction of Risk of Wet-Pavement Accidents: Fuzzy Logic Model." Transportation Research Record: Journal of the Transportation Research Board 1717, no. 1 (January 2000): 28–36. http://dx.doi.org/10.3141/1717-05.

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Researchers developed a fuzzy-logic model for predicting the risk of accidents that occur on wet pavements. Preventing wet-pavement accidents has been an extremely difficult and elusive task because they are stochastic events whose occurrence is related to a variety of factors, including vehicle, roadway, human, and environmental characteristics. Conventionally, researchers use linear or nonlinear regression models and probabilistic models to predict wet-pavement accidents. However, these models often are limited in their capability to fully explain the process when the underlying physical system possesses a degree of non-linearity. Therefore, the potential of applying fuzzy logic in this area might be promising. Two fuzzy-logic models were developed and evaluated using accident data and the corresponding traffic data collected from 123 sections of highway in Pennsylvania from 1984 to 1986. The models use skid number, posted speed, average daily traffic, percentage of wet time, and driving difficulty as input variables and the number of wet-pavement accidents as the output variable. The first model is based on Mamdani’s fuzzy-inference method, and the second is a Sugeno-type fuzzy-logic model using the fuzzy-clustering method. The two fuzzy-logic models show superiority over the probabilistic model and the nonlinear regression model. Results indicate that, in addition to predicting the risk of wet-pavement accidents, the fuzzy-logic model can be applied conveniently to determine specific corrective actions that should be undertaken to improve safety.

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Isdore Onyema Akwukwaegbu, Eleazar Benson Mfonobong, Jude-Kennedy Chibuzo Obichere, and Chiedozie Francis Paulinus-Nwammuo. "Smart fuzzy logic-based model of traffic light management." World Journal of Advanced Engineering Technology and Sciences 8, no. 2 (April 30, 2023): 344–58. http://dx.doi.org/10.30574/wjaets.2023.8.2.0108.

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Traffic congestion difficulties have resulted in low productivity, significant air pollution, and energy losses in Owerri Metropolis, Nigeria. The paper looked at the design of a smart fuzzy logic traffic light management module, the development of a traffic control program using an Arduino microcontroller system, and the validation of the developed program's functionality using a Proteus circuit model to confirm the efficiency of fuzzy signal control. The traffic light environment of a fuzzy logic controller is simulated using Matlab software, and isolated traffic of multiple junctions is simulated using the Sumo Urban Mobility Simulation (SUMO) environment. The performance of the traditional fixed-time controller vs the fuzzy logic traffic light controller is examined. To allocate pedestrian crossing the right of way, traffic light systems function in tandem with pedestrian displays. The simulation results indicated that the overall durations for the fixed traffic light controller and suggested smart fuzzy logic traffic light controller simulations are 1,426 seconds and 1,328 seconds, respectively. The results also showed that the suggested smart fuzzy logic traffic light controller recorded significant waiting and movement times, indicating its standard appropriateness for Owerri city's various crossings.

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Huynh, V. N., Y. Nakamori, T. B. Ho, and G. Resconi. "A context model for fuzzy concept analysis based upon modal logic." Information Sciences 160, no. 1-4 (March 2004): 111–29. http://dx.doi.org/10.1016/j.ins.2003.08.010.

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N. Zohedi, Fauzal, M. A.Rahmat, and Hyreil A.Kasdirin. "A Critical Analysis of Fuzzy Logic Controller for Slip Control in Antilock Braking System (ABS)." International Journal of Engineering & Technology 7, no. 3.28 (August 17, 2018): 116. http://dx.doi.org/10.14419/ijet.v7i3.28.20981.

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This project aims at proposing an innovative way to implement the concept of fuzzy logic to an ABS model. The implementation of this project was conducted using simulation of ABS which is a combination from vehicle speed, wheel speed and slip through MATLAB Simulink software. By implementing fuzzy logic to the ABS system, the fuzzy logic can facilitate in improving the ABS abilities. The ABS model is developed and fuzzy logic controller is implemented to the model. The performance of the Fuzzy ABS is analyzed. The result shows that the fuzzy logic controller can facilitates the performance of the ABS by reducing the stopping time and maintaining the slip value near to 0.2.

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Ma, Zhanyou, Zhaokai Li, Weijun Li, Yingnan Gao, and Xia Li. "Model Checking Fuzzy Computation Tree Logic Based on Fuzzy Decision Processes with Cost." Entropy 24, no. 9 (August 24, 2022): 1183. http://dx.doi.org/10.3390/e24091183.

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In order to solve the problems in fuzzy computation tree logic model checking with cost operator, we propose a fuzzy decision process computation tree logic model checking method with cost. Firstly, we introduce a fuzzy decision process model with cost, which can not only describe the uncertain choice and transition possibility of systems, but also quantitatively describe the cost of the systems. Secondly, under the model of the fuzzy decision process with cost, we give the syntax and semantics of the fuzzy computation tree logic with cost operators. Thirdly, we study the problem of computation tree logic model checking for fuzzy decision process with cost, and give its matrix calculation method and algorithm. We use the example of medical expert systems to illustrate the method and model checking algorithm.

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PERLOVSKY, LEONID I. "FUZZY DYNAMIC LOGIC." New Mathematics and Natural Computation 02, no. 01 (March 2006): 43–55. http://dx.doi.org/10.1142/s1793005706000300.

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Fuzzy logic is extended toward dynamic adaptation of the degree of fuzziness. The motivation is to explain the process of learning as a joint model improvement and fuzziness reduction. A learning system with fuzzy models is introduced. Initially, the system is in a highly fuzzy state of uncertain knowledge, and it dynamically evolves into a low-fuzzy state of certain knowledge. We present an image recognition example of patterns below clutter. The paper discusses relationships to formal logic, fuzzy logic, complexity and draws tentative connections to Aristotelian theory of forms and working of the mind.

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Janková, Zuzana, and Petr Dostál. "Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty." Mathematical Problems in Engineering 2021 (June 18, 2021): 1–16. http://dx.doi.org/10.1155/2021/3839071.

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Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited descriptiveness. This paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets may include additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs are calculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the level of uncertainty contained in fuzzy sets and compared with the type-1 fuzzy model. The case study aims to create a model to support investment decisions in Exchange-Traded Funds (ETFs) listed on international equity markets. The created models of type-2 fuzzy logic are compared with the classic type-1 fuzzy logic model. Based on the results of the comparison, it can be said that type-2 fuzzy logic with dual fuzzy sets is able to better describe data from financial time series and provides more accurate outputs. The results reflect the capability and effectiveness of the approach proposed in this document. However, the performance of type-2 fuzzy logic models decreases with the inclusion of increasing uncertainty in fuzzy sets. For further research, it would be appropriate to examine the different levels of uncertainty in the input parameters themselves and monitor the performance of such a modified model.

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Janková, Zuzana, Dipak Kumar Jana, and Petr Dostál. "Investment Decision Support Based on Interval Type-2 Fuzzy Expert System." Engineering Economics 32, no. 2 (April 29, 2021): 118–29. http://dx.doi.org/10.5755/j01.ee.32.2.24884.

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The decision-making process on investing in financial markets is a very complex and difficult task, mainly due to the chaotic behavior and high uncertainty in the development of the prices of investment instruments. For this reason, financial markets are increasingly using means of artificial intelligence, namely fuzzy logic, which is able to capture the nonlinear behavior.Fuzzy logic provides a way to draw definitive conclusions from vague, ambiguous, or inaccurate information.However, there are some drawbacks associated with type-1 fuzzy logic, so the type-2 fuzzy logic comes forward, which can work with greater uncertainty. Type-2 fuzzy logic works with a new third dimension fuzzy set that provides additional degrees of freedom and allows to model and process numerical and linguistic uncertainties directly. The paper applies type-2 fuzzy logic to the stock market with the aim to create a simple and understandable model for deciding on investing in investment instruments, which is important for investors in this area. The proposed type-2 fuzzy model uses return, risk, dividend and total expense ratio of ETF as input variables. The created system is able to generate aggregated models from a certain number of language rules, which allows the investor to understand the created financial model. Using type-2 fuzzy logic can lead to more realistic and accurate results than type-1 fuzzy logic.

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Haghighat, Ezzatollah, Saeed Shaikhzadeh Najar, and Seyed Mohammad Etrati. "The Prediction of Needle Penetration Force in Woven Denim Fabrics Using Soft Computing Models." Journal of Engineered Fibers and Fabrics 9, no. 4 (December 2014): 155892501400900. http://dx.doi.org/10.1177/155892501400900406.

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The aim of this paper was to predict the needle penetration force in denim fabrics based on sewing parameters by using the fuzzy logic (FL) model. Moreover, the performance of fuzzy logic model is compared with that of the artificial neural network (ANN) model. The needle penetration force was measured on the Instron tensile tester. In order to plan the fuzzy logic model, the sewing needle size, number of fabric layers and fabric weight were taken into account as input parameters. The output parameter is needle penetration force. In addition, the same parameters and data are used in artificial neural network model. The results indicate that the needle penetration force can be predicted in terms of sewing parameters by using the fuzzy logic model. The difference between performance of fuzzy logic and neural network models is not meaningful ( RFL=0.971 and RANN=0.982). It is concluded that soft computing models such as fuzzy logic and artificial neural network can be utilized to forecast the needle penetration force in denim fabrics. Using the fuzzy logic model for predicting the needle penetration force in denim fabrics can help the garment manufacturer to acquire better knowledge about the sewing process. As a result, the sewing process may be improved, and also the quality of denim apparel increased.

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Petrović, Dejan V., Miloš Tanasijević, Saša Stojadinović, Jelena Ivaz, and Pavle Stojković. "Fuzzy Model for Risk Assessment of Machinery Failures." Symmetry 12, no. 4 (April 3, 2020): 525. http://dx.doi.org/10.3390/sym12040525.

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The main goal of this research was the development of an algorithm for the implementation of negative risk parameters in a synthesis model for a risk level assessment for a specific machine used in the mining industry. Fuzzy sets and fuzzy logic theory, in combination with statistical methods, were applied to analyze the time picture state of the observed machine. Fuzzy logic is presented through fuzzy proposition and a fuzzy composition module. Using these tools, the symmetric position of the fuzzy sets with regard to class was used, and the symmetric fuzzy inference approach was used in an outcome calculation. The main benefit of the proposed model is being able to use numerical and linguistic data in a risk assessment model. The proposed risk assessment model, using fuzzy logic conclusions and min–max composition, was used on a mobile crushing machine. The results indicated that the risk level of the mobile crushing machine was in the “high” category, which means that it is necessary to introduce maintenance policies based on this high risk. The proposed risk assessment model is useful for any engineering system.

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Könczöl, Boldizsár, and László Gál. "Analysing fuzzy logic-based line following model car." Mérnöki és Informatikai Megoldások, no. II. (October 20, 2020): 21–31. http://dx.doi.org/10.37775/eis.2020.2.3.

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In our previous work a fuzzy logic-based controller was successfully applied to a line following model car utilizing Arduino Uno. Regarding fuzzy operations (t-norms), this logic has several implementations and our aim was to show how functional can be the chosen ones, and whether there are any remarkable differences among them. The fuzzy rules were very easy to create, except the drastic t-norm, all of them completed the tests, thus it can be stated that using fuzzy logic is convenient for line following. In this paper we focus on the impact of using a more capable microcontroller (Espressif ESP32) based board for the controller. Imrpovement of results is expected because of the higher computing performance of this board.

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Zahra, Latifah, Maiyastri Maiyastri, and Izzati Rahmi. "A COMPARISON OF FUZZY TIME SERIES CHENG AND CHEN-HSU IN FORECASTING TOTAL AIRPLANE PASSENGERS OF SOEKARNO-HATTA AIRPORT." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 1 (March 13, 2024): 0019–28. http://dx.doi.org/10.30598/barekengvol18iss1pp0019-0028.

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In some cases, the demand for flights has increased or decreased unexpectedly. Based on this airport as a service provider balance the availability of the service and the needs in the field. To balance all the provided services, the airport needs to predict the total passenger that would visit the airport on consecutive days. Thus, a form of time-series forecast is used in this research. We applied fuzzy time series (FTS) to forecasting total airplane passengers, where there are several logics in FTS including FTS Cheng’s Logic and FTS Chen-Hsu’s Logic. To determine the accuracy of the forecast, use three criteria, namely Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE). In terms of modelling and forecasting data, FTS Chen-Hsu’s Logic is better than FTS Cheng’s Logic. This is shown in the value of three accuracy criteria of FTS Chen-Hsu’s Logic are smaller than FTS Cheng’s Logic. Conclusion, FTS Chen-Hsu method can be used as a forecasting model for the total passenger airplane in Soekarno-Hatta International Airport

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