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Journal articles on the topic 'Fuzzy computation'

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Published: 10 December 2022
Last updated: 16 July 2024

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1

Wechler, Wolfgang. "R-fuzzy computation." Journal of Mathematical Analysis and Applications 115, no. 1 (April 1986): 225–32. http://dx.doi.org/10.1016/0022-247x(86)90036-3.

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2

Dubois, Didier, and Henri Prade. "Handbook of Fuzzy Computation,." Fuzzy Sets and Systems 123, no. 3 (November 2001): 397–98. http://dx.doi.org/10.1016/s0165-0114(01)00092-6.

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3

Gerla, Giangiacomo. "Theory of fuzzy computation." International Journal of General Systems 44, no. 4 (February 19, 2015): 519–21. http://dx.doi.org/10.1080/03081079.2014.1000641.

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4

Oussalah, M. "Approximated fuzzy LR computation." Information Sciences 153 (July 2003): 155–75. http://dx.doi.org/10.1016/s0020-0255(03)00071-9.

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5

Ross, Timothy, and Jonathan Lucero. "Handbook of Fuzzy Computation." Pattern Analysis & Applications 4, no. 1 (March 2001): 77. http://dx.doi.org/10.1007/s100440170031.

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6

LIAU, CHURN JUNG, and BERTRAND I.-PENG LIN. "FUZZY LOGIC WITH EQUALITY." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 02 (June 1988): 351–65. http://dx.doi.org/10.1142/s0218001488000212.

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The concept of fuzzy equality and its related contents to the first order predicate calculus are discussed. It is proved that, in the viewpoint of computational logic, resolution and paramodulation mechanisms are complete and sound for fuzzy logic with equality. Term rewriting system, that is the set of left to right directional equations, provides an essential computational paradigm for word problems in universal algebra. We embody the fuzzy equality to the theory of this computation system and give an algorithmic solution to the word problems in fuzzy algebra.
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7

D'hooghe, Bart, Jarosław Pykacz, and Roman R. Zapatrin. "Quantum Computation of Fuzzy Numbers." International Journal of Theoretical Physics 43, no. 6 (June 2004): 1423–32. http://dx.doi.org/10.1023/b:ijtp.0000048625.57350.4f.

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8

Sandoval, Francisco, Zeng-Guang Hou, and Madan M. Gupta. "Fuzzy-neural Computation and Robotics." Soft Computing 11, no. 3 (March 3, 2006): 211. http://dx.doi.org/10.1007/s00500-006-0061-y.

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9

Wang, Jing-Zhong, Yingchieh Ho, and Tsung-Ying Sun. "Fuzzy Scaled Mutation Evolutionary Computation." International Journal of Fuzzy Systems 18, no. 6 (February 18, 2016): 1162–79. http://dx.doi.org/10.1007/s40815-016-0155-3.

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10

Sharma, Kapil, Varsha Dixit, Brijesh Kumar Chaurasia, and Shekhar Verma. "Trust computation using fuzzy analyser." International Journal of Information Technology, Communications and Convergence 3, no. 3 (2019): 177. http://dx.doi.org/10.1504/ijitcc.2019.10028182.

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Sharma, Kapil, Varsha Dixit, Brijesh Kumar Chaurasia, and Shekhar Verma. "Trust computation using fuzzy analyser." International Journal of Information Technology, Communications and Convergence 3, no. 3 (2019): 177. http://dx.doi.org/10.1504/ijitcc.2019.106556.

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12

Sarna, Marian. "Fuzzy relations on fuzzy and non-fuzzy numbers-fast computation formulas." Fuzzy Sets and Systems 29, no. 2 (January 1989): 155–63. http://dx.doi.org/10.1016/0165-0114(89)90189-9.

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13

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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14

M., Inbavalli. "Fuzzy Inference Model for Computation and Prediction of Disease Pattern." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 672–79. http://dx.doi.org/10.5373/jardcs/v12sp4/20201533.

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15

K., Pandey M., Dandotiya A., Trivedi M. K., Bhadoriya S. S., and Ramasesh G. R. "Delay Computation Using Fuzzy Logic Approach." International Journal of Intelligent Systems and Applications 4, no. 11 (October 9, 2012): 84–90. http://dx.doi.org/10.5815/ijisa.2012.11.10.

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16

Pan, Haiyu, Yongming Li, Yongzhi Cao, and Zhanyou Ma. "Model checking fuzzy computation tree logic." Fuzzy Sets and Systems 262 (March 2015): 60–77. http://dx.doi.org/10.1016/j.fss.2014.07.008.

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17

ASCIA, G., and V. CATANIA. "AN EFFICIENT HARDWARE ARCHITECTURE TO SUPPORT COMPLEX FUZZY REASONING." International Journal on Artificial Intelligence Tools 05, no. 01n02 (June 1996): 41–60. http://dx.doi.org/10.1142/s0218213096000043.

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The paper presents the design of a VLSI fuzzy processor which is capable of supporting complex fuzzy reasoning. The architecture of the processor is based on a appropriate computational model, whose main features are: capability to cope with rule chaining; pre-processing of inferences to reduce the number of rules to be processed; parallel computation of the degree of activation of the rules; optimized representation of membership function. The processor performance is in the order of 1.5 MFLIPS (256 rule, 8 Fuzzy inputs, 4 output).
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18

Syropoulos, Apostolos. "A (Basis for a) Philosophy of a Theory of Fuzzy Computation." Kairos. Journal of Philosophy & Science 20, no. 1 (June 1, 2018): 181–201. http://dx.doi.org/10.2478/kjps-2018-0009.

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Abstract Vagueness is a linguistic phenomenon as well as a property of physical objects. Fuzzy set theory is a mathematical model of vagueness that has been used to define vague models of computation. The prominent model of vague computation is the fuzzy Turing machine. This conceptual computing device gives an idea of what computing under vagueness means, nevertheless, it is not the most natural model. Based on the properties of this and other models of vague computing, an attempt is made to formulate a basis for a philosophy of a theory of fuzzy computation.
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19

Ramya, H. R., and B. K. Sujatha. "Real Time Image Fusion Based Technique for Medical Images." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4500–4508. http://dx.doi.org/10.1166/jctn.2020.9105.

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To tackle the cost of storage and storage space with fast-growing technologies, the image fusion is playing an important role in several image-processing areas such as medical-imaging and satelliteimaging. This fused picture is appropriate for machine perception, human visual analysis or further analysis assignment. Recently the computing method such as fuzzy logic model has been extensively used in the field of image-processing due to the uniqueness of handling uncertain modeling. The fuzzy logic based image-fusion model generally performed better with respect to other existing image fusion models. In this paper, we considered type-2 fuzzy logic, which has similar function to earlier fuzzy logic technique but consist more functionality that allows optimized management of higher degrees under uncertainty. Interval type-2 fuzzy-logic-system (IT2FLS) are widely used fuzzy sets due to their ease of use and computational simplicity. A real time image fusion (RTIF) technique that is based on the IT2FLS is used to overcome the excess computation time and nonlinear uncertainties, which is present in the medical images. In the result simulation section, we have shown that our proposed model has taken less computation time and provided better quality assessment matrices with respect to existing system.
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20

Giakoumakis, Stylianos, and Basil Papadopoulos. "An Algorithm for Fuzzy Negations Based-Intuitionistic Fuzzy Copula Aggregation Operators in Multiple Attribute Decision Making." Algorithms 13, no. 6 (June 26, 2020): 154. http://dx.doi.org/10.3390/a13060154.

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In this paper, we develop a novel computation model of Intuitionistic Fuzzy Values with the usage of fuzzy negations and Archimedean copulas. This novel computation model’s structure is based on the extension of the existing operations of intuitionistic fuzzy values with some classes of fuzzy negations. Many properties of the proposed operations are investigated and proved. Additionally, in this paper we introduce the concepts of intuitionistic fuzzy Archimedean copula weighted arithmetic and geometric aggregation operators based on fuzzy negations, including a further analysis of their properties. Finally, using a case study from an already published paper we found that our method has many advantages.
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21

Widyanto, M. Rahmat, and Tatik Maftukhah. "Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 1 (January 20, 2010): 34–38. http://dx.doi.org/10.20965/jaciii.2010.p0034.

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Fuzzy relevance feedback using Query Vector Modification (QVM) method in image retrieval is proposed. For feedback, the proposed six relevance levels are: “very relevant”, “relevant”, “few relevant”, “vague”, “not relevant”, and “very non relevant”. For computation of user feedback result, QVM method is proposed. The QVM method repeatedly reformulates the query vector through user feedback. The system derives the image similarity by computing the Euclidean distance, and computation of color parameter value by Red, Green, and Blue (RGB) color model. Five steps for fuzzy relevance feedback are: image similarity, output image, computation of membership value, feedback computation, and feedback result. Experiments used QVM method for six relevance levels. Fuzzy relevance feedback using QVM method gives higher precision value than conventional relevance feedback method. Experimental results show that the precision value improved by 28.56% and recall value improved 3.2% of conventional relevance feedback. That indicated performance Image Retrieval System can be improved by fuzzy relevance feedback using QVM method.
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22

Güneş, Mustafa, and Nurullah Umarosman. "Fuzzy Goal Programming Approach on Computation of the Fuzzy Arithmetic Mean." Mathematical and Computational Applications 10, no. 2 (August 1, 2005): 211–20. http://dx.doi.org/10.3390/mca10020211.

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23

Abbasi, Fazlollah, Tofigh Allahviranloo, and Muhammad Akram. "A New Framework for Numerical Techniques for Fuzzy Nonlinear Equations." Axioms 12, no. 2 (February 20, 2023): 222. http://dx.doi.org/10.3390/axioms12020222.

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This paper describes a computational method for solving the nonlinear equations with fuzzy input parameters that we encounter in engineering system analysis. In addition to discussing the existence of solutions, the definition and formalization of numerical solutions is based on a new fuzzy computation operation as a transmission average. Error analysis in numerical solutions is described. Finally, some examples are presented to implement the proposed method and its effectiveness compared to other previous methods.
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24

LIU, KEVIN F. R., JONATHAN LEE, and WEILING CHIANG. "HIGH-LEVEL FUZZY PETRI NETS AS A BASIS FOR MANAGING SYMBOLIC AND NUMERICAL INFORMATION." International Journal on Artificial Intelligence Tools 09, no. 04 (December 2000): 569–88. http://dx.doi.org/10.1142/s0218213000000367.

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The focus of this paper is on an attempt towards a unified formalism to manage both symbolic and numerical information based on high-level fuzzy Petri nets (HLFPN). Fuzzy functions, fuzzy reasoning, and fuzzy neural networks are integrated in HLFPN In HLFPN model, a fuzzy place carries information to describe the fuzzy variable and the fuzzy set of a fuzzy condition. An arc is labeled with a fuzzy weight to represent the strength of connection between places and transitions. A fuzzy set and a fuzzy truth-value are attached to an uncertain fuzzy token to model imprecision and uncertainty. We have identified six types of uncertain transition: calculation transitions to compute functions with uncertain fuzzy inputs; inference transitions to perform fuzzy reasoning; neuron transitions to execute computations in neural networks; duplication transitions to duplicate an uncertain fuzzy token to several tokens carrying the same fuzzy sets and fuzzy truth values; aggregation transitions to combine several uncertain fuzzy tokens with the same fuzzy variable; and aggregation-duplication transitions to amalgamate aggregation transitions and duplication transitions. To guide the computation inside the HLFPN, an algorithm is developed and an example is used to illustrate the proposed approach.
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25

Xu, Renbo, Wanrong Zhang, and Zongsheng Nie. "Fuzzy Computation of Solar Hybrid Electric Vehicle." Journal of Physics: Conference Series 1648 (October 2020): 042119. http://dx.doi.org/10.1088/1742-6596/1648/4/042119.

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26

Lazzerini, Beatrice, and Francesco Marcelloni. "Reducing computation overhead in MISO fuzzy systems." Fuzzy Sets and Systems 113, no. 3 (August 2000): 485–96. http://dx.doi.org/10.1016/s0165-0114(98)00111-0.

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27

Jiménez, F., J. M. Cadenas, G. Sánchez, A. F. Gómez-Skarmeta, and J. L. Verdegay. "Multi-objective evolutionary computation and fuzzy optimization." International Journal of Approximate Reasoning 43, no. 1 (September 2006): 59–75. http://dx.doi.org/10.1016/j.ijar.2006.02.001.

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28

Bertini, C., and R. Leporini. "A fuzzy approach to quantum logical computation." Fuzzy Sets and Systems 317 (June 2017): 44–60. http://dx.doi.org/10.1016/j.fss.2016.06.004.

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29

Loor, Marcelo, and Guy De Tré. "Handling subjective information through augmented (fuzzy) computation." Fuzzy Sets and Systems 391 (July 2020): 47–71. http://dx.doi.org/10.1016/j.fss.2019.05.007.

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30

Gerla, Giangiacomo. "Comments on some theories of fuzzy computation." International Journal of General Systems 45, no. 4 (November 7, 2015): 372–92. http://dx.doi.org/10.1080/03081079.2015.1076403.

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31

Ishak, Fuziyah, and Najihah Chaini. "Numerical computation for solving fuzzy differential equations." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (November 1, 2019): 1026. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp1026-1033.

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Fuzzy differential equations (FDEs) play important roles in modeling dynamic systems in science, economics and engineering. The modeling roles are important because most problems in nature are indistinct and uncertain. Numerical methods are needed to solve FDEs since it is difficult to obtain exact solutions. Many approaches have been studied and explored by previous researchers to solve FDEs numerically. Most FDEs are solved by adapting numerical solutions of ordinary differential equations. In this study, we propose the extended Trapezoidal method to solve first order initial value problems of FDEs. The computed results are compared to that of Euler and Trapezoidal methods in terms of errors in order to test the accuracy and validity of the proposed method. The results shown that the extended Trapezoidal method is more accurate in terms of absolute error. Since the extended Trapezoidal method has shown to be an efficient method to solve FDEs, this brings an idea for future researchers to explore and improve the existing numerical methods for solving more general FDEs.
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32

Kumar, Dhiraj, and Manoranjan Kumar Singh. "ON THE NUMBER OF FUZZY SUBGROUPS AND FUZZY NORMAL SUBGROUPS OF S2, S3 AND A4." South East Asian J. of Mathematics and Mathematical Sciences 19, no. 01 (April 30, 2023): 277–86. http://dx.doi.org/10.56827/seajmms.2023.1901.23.

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Counting fuzzy subgroups of a finite group is a fundamental problem of fuzzy group theory. Many researchers have made significant contributions to the rapid growth of this topic in recent years. The number of fuzzy subgroups of any group is infinite without the aid of equivalence relation. Some authors have used the equivalence relation of fuzzy sets to study the equivalence of fuzzy subgroups ([5], [6], [16]). The problem of counting the number of distinct fuzzy subgroups of a finite group is relative to the choice of the equivalence relation. The number of fuzzy subgroups of a particular group varies from one equivalence relation to the other. The equivalence relation applied in our computation can be seen in the existing literature. Sulaiman and Abd Ghafur [10] define an equivalence relation for counting fuzzy subgroups of group G. We have used this relation to find fuzzy subgroups and fuzzy normal subgroups of S2, S3 and A4. Lattice subgroup diagrams were used in our computation.
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33

Bustince, H., E. Barrenechea, M. Pagola, and R. Orduna. "Image Thresholding Computation Using Atanassov’s Intuitionistic Fuzzy Sets." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 2 (February 20, 2007): 187–94. http://dx.doi.org/10.20965/jaciii.2007.p0187.

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In this paper, a new thresholding technique using Atanassov’s intuitionistic fuzzy sets (A-IFSs) and restricted dissimilarity functions is introduced. In recent years, various thresholding techniques ([18, 24]) based on fuzzy set theory have been introduced to overcome the problem of non-uniform illumination and inherent image vagueness. In this paper we analyze this task and propose a new method for handling the grayness ambiguity and vagueness during the process of threshold selection.
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34

Qin, Hongwu, Chengjun Gu, Xiuqin Ma, Weiyi Wei, and Yibo Wang. "S-Score Table-Based Parameter-Reduction Approach for Fuzzy Soft Sets." Symmetry 14, no. 8 (August 17, 2022): 1719. http://dx.doi.org/10.3390/sym14081719.

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A fuzzy soft set is a mathematical tool used to deal with vagueness and uncertainty. Parameter reduction is an important issue when applying a fuzzy soft set to handle decision making. However, existing methods neglect newly added parameters and have higher computational complexities. In this paper, we propose a new S-Score table-based parameter-reduction approach for fuzzy soft sets. Compared with two existing methods of parameter reduction for a fuzzy soft set, our method takes newly added parameters into account, which brings about greater flexibility and is beneficial to the extension of fuzzy soft sets and a combination of multiple fuzzy soft sets. Additionally, our method accesses fewer elements from the dataset, which results in lower computation compared with the two existing approaches. The experimental results from two applications show the availability and feasibility of our approach.
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35

Mustafa, Saima, Shumaila Ghaffar, Murrium Bibi, Muhammad Ghaffar Khan, Qaisara Praveen, Harish Garg, and Mahamane Saminou. "An Application of Fuzzy Multiple Linear Regression in Biological Paradigm." Complexity 2022 (September 14, 2022): 1–6. http://dx.doi.org/10.1155/2022/1162464.

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The regression model is generally utilized in several fields of study because of its applications. Regression is an extremely incredible approach; it builds up a connection between dependent and independent variables. We have addressed a powerful computational model by utilizing dengue information joined with fuzzy multiple linear regression. Information is accumulated on dengue fever through the survey. This paper is centered on the comparison of the crisp method with fuzzy multiple linear regression, and then, the utilization of a fuzzy multiple regression method is explained after the comparison. We have used multiple regression and then converted the said technique into three fuzzy cases. The effectiveness of the fuzzy multiple regression model is measured by numerical computation and comparison of both techniques. 2020 Mathematics Subject Classification. Primary 30C45; 30C50; 30C80; Secondary 11B65, 47B38.
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36

Yu, Le, and Yumei Chen. "Solving Fuzzy Volterra Integro-Differential Equations By Using Fuzzy Kamal Transform." International Journal of Research in Advent Technology 10, no. 4 (December 30, 2022): 1–5. http://dx.doi.org/10.32622/ijrat.104202201.

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In this paper, the fuzzy Kamal transform is used to solve fuzzy Volterra integral-differential equations, which is based on Kamal transform. Kamal transform takes very little computation and time. Numerical examples are given to prove the effectiveness of Kamal transform in solving fuzzy Volterra integro-differential equations
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37

Meena, V., Obulaporam Gireesha, Kannan Krithivasan, and V. S. Shankar Sriram. "Fuzzy simplified swarm optimization for multisite computational offloading in mobile cloud computing." Journal of Intelligent & Fuzzy Systems 39, no. 6 (December 4, 2020): 8285–97. http://dx.doi.org/10.3233/jifs-189148.

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Mobile Cloud Computing (MCC)’s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. However, their processing speeds, access delays, computation capability, residual memory and service charges are different which retards their usage, as it becomes time-consuming and ambiguous for making decisions. To address the aforementioned issues, this paper presents a Fuzzy Simplified Swarm Optimization based cloud Computational Offloading (FSSOCO) algorithm to achieve optimum multisite offloading. Fuzzy logic and simplified swarm optimization are employed for the identification of high powerful nodes and task decomposition respectively. The overall performance of FSSOCO is validated using the Specjvm benchmark suite and compared with the state-of-the-art offloading techniques in terms of the weighted total cost, energy consumption, and processing time.
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38

Qing, Ming. "The Unique Representations of Fuzzy Entropy for a Finite Fuzzy Set." Applied Mechanics and Materials 433-435 (October 2013): 766–69. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.766.

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Many methods were presented to define fuzzy entropy to measure fuzzy degree of a fuzzy set and a variety of fuzzy entropy formulae were derived and constructed from the definitions of fuzzy entropy. In this paper, a new definition of fuzzy entropy is presented based on a simple order relation and computation formulae of fuzzy entropy is given. Then, the unique representations of fuzzy entropy are given by applying several set of reasonable conditions to fuzzy entropy.
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39

Rosyida, Isnaini, and Suryono Suryono. "Coloring picture fuzzy graphs through their cuts and its computation." International Journal of Advances in Intelligent Informatics 7, no. 1 (March 31, 2021): 63. http://dx.doi.org/10.26555/ijain.v7i1.612.

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In a fuzzy set (FS), there is a concept of alpha-cuts of the FS for alpha in [0,1]. Further, this concept was extended into (alpha,delta)-cuts in an intuitionistic fuzzy set (IFS) for delta in [0,1]. One of the expansions of FS and IFS is the picture fuzzy set (PFS). Hence, the concept of (alpha,delta)-cuts was developed into (alpha,delta,beta)-cuts in a PFS where beta is an element of [0,1]. Since a picture fuzzy graph (PFG) consists of picture fuzzy vertex or edge sets or both of them, we have an idea to construct the notion of the (alpha,delta,beta)-cuts in a PFG. The steps used in this paper are developing theories and algorithms. The objectives in this research are to construct the concept of (alpha,delta,beta)-cuts in picture fuzzy graphs (PFGs), to construct the (alpha,delta,beta)-cuts coloring of PFGs, and to design an algorithm for finding the cut chromatic numbers of PFGs. The first result is a definition of the (alpha,delta,beta)-cut in picture fuzzy graphs (PFGs) where (alpha,delta,beta) are elements of a level set of the PFGs. Further, some properties of the cuts are proved. The second result is a concept of PFG coloring and the chromatic number of PFG based on the cuts. The third result is an algorithm to find the cuts and the chromatic numbers of PFGs. Finally, an evaluation of the algorithm is done through Matlab programming. This research could be used to solve some problems related to theories and applications of PFGs.
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40

Zhou, Yan, Hongwei Guo, Dongli Wang, and Chunjiang Liao. "An adaptive eco with weighted feature for visual tracking." Filomat 34, no. 15 (2020): 5139–48. http://dx.doi.org/10.2298/fil2015139z.

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The efficient convolution operator (ECO) have manifested predominant results in visual object tracking. However, in the pursuit of performance improvement, the computational burden of the tracker becomes heavy, and the importance of different feature layers is not considered. In this paper, we propose a self-adaptive mechanism for regulating the training process in the first frame. To overcome the over-fitting in the tracking process, we adopt the fuzzy model update strategy. Moreover, we weight different feature maps to enhance the tracker performance. Comprehensive experiments have conducted on the OTB-2013 dataset. When adopting our ideas to adjust our tracker, the self-adaptive mechanism can avoid unnecessary training iterations, and the fuzzy update strategy reduces one fifth tracking computation compared to the ECO. Within reduced computation, the tracker based our idea incurs less than 1% loss in AUC (area-undercurve).
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41

Fan, Yongqing, Keyi Xing, and Xiangkui Jiang. "Fuzzy Adaptation Algorithms’ Control for Robot Manipulators with Uncertainty Modelling Errors." Complexity 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/5468090.

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A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.
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42

Sudakov, Vladimir, and Alexander Zhukov. "Fuzzy Domination Graphs in Decision Support Tasks." Mathematics 11, no. 13 (June 24, 2023): 2837. http://dx.doi.org/10.3390/math11132837.

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In decision support tasks, one often has to deal with uncertainty due to fuzzy judgments of the decision maker or the expert. This paper proposes methods that allow you to rank the alternatives based on fuzzy evaluations. This is achieved by using fuzzy weighted summation, fuzzy implication, a computation graph showing the criteria, and a fuzzy dominance graph showing the alternatives. If the criteria have equal importance, then fuzzy graphs corresponding to the dominance of each of the criteria are used. An algorithm that is used for both the transition from fuzzy dominance graphs and the ranking of alternatives is proposed. This algorithm is based on the idea of constructing Kemeny medians or other concordant rankings at a given confidence level in the existence of corresponding arcs. Computational experiments have shown the performance of these approaches. It is reasonable to apply them in problems that require complex expert evaluations with a large number of alternatives and criteria.
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43

Hao, Guo Sheng, Xiang Jun Zhao, and Yong Qing Huang. "Users' Fuzzy Cognition Knowledge Learning in Interactive Evolutionary Computation and its Application." Advanced Materials Research 204-210 (February 2011): 245–50. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.245.

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user in interactive evolutionary computation (IEC) has the characteristic of fuzzy cognition. Based on this, a method to learn users’ fuzzy cognition knowledge is given. The method includes the fuzzy expression of the basic elements of IEC such as search space, population, gene sense unit and so on. Then a method to increase the performance of IEC based on the knowledge of users’ fuzzy cognition is given. The above results enrich the researches of IEC users' cognition.
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44

Abughurra, Sana. "Analytical solutions forfuzzysystem using power series approach." JOURNAL OF ADVANCES IN MATHEMATICS 12, no. 8 (September 15, 2016): 6553–59. http://dx.doi.org/10.24297/jam.v12i8.5973.

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The aim of the present paper is present a relatively new analytical method, called residual power series (RPS) method, for solving system of fuzzy initial value problems under strongly generalized differentiability. The technique methodology provides the solution in the form of a rapidly convergent series with easily computable components using symbolic computation software. Several computational experiments are given to show the good performance and potentiality of the proposed procedure. The results reveal that the present simulated method is very effective, straightforward and powerful methodology to solve such fuzzy equations.
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45

Yadav, Swati, S. P. Tiwari, Mausam Kumari, and Vijay K. Yadav. "An Interval Type-2 Fuzzy Model of Computing with Words via Interval Type-2 Fuzzy Finite Rough Automata with Application in COVID-19 Deduction." New Mathematics and Natural Computation 18, no. 01 (March 2022): 61–101. http://dx.doi.org/10.1142/s1793005722500053.

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Classical automata, fuzzy automata, and rough automata with input alphabets as numbers or symbols are formal computing models with values. Fuzzy automata and rough automata are computation models with uncertain or imprecise information about the next state and can only process the string of input symbols or numbers. To process words and propositions involved in natural languages, we need a computation model to model real-world problems by capturing the uncertainties involved in a word. In this paper, we have shown that computing with word methodology deals with perceptions rather than measurements and allows the use of words in place of numbers and symbols while describing the real-world problems together with interval type-2 (IT2) fuzzy sets which have the capacity to capture uncertainties involved in word using its footprint of uncertainty. The rough set theory, which has potential of modeling vagueness in the imprecise and ill-defined environment, introduces a computation model, namely, IT2 fuzzy rough finite automata, which is efficient to process uncertainties involved in words. Further, we have shown the application of introduced IT2 fuzzy finite rough automaton in the medical diagnosis of COVID-19 patients.
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46

Buonsanti, M., M. Cacciola, S. Calcagno, F. C. Morabito, and M. Versaci. "Fuzzy computation for classifying defects in metallic plates." International Journal of Applied Electromagnetics and Mechanics 25, no. 1-4 (May 10, 2007): 325–31. http://dx.doi.org/10.3233/jae-2007-786.

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47

Kumar, Rupak, and Hina Tahir. "A Fuzzy based approach for function point computation." Global Sci-Tech 9, no. 4 (2017): 246. http://dx.doi.org/10.5958/2455-7110.2017.00033.7.

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48

Natarajan, Sivaramakrishnan, Subramaniyaswamy Vairavasundaram, and Logesh Ravi. "Optimized fuzzy-based group recommendation with parallel computation." Journal of Intelligent & Fuzzy Systems 36, no. 5 (May 14, 2019): 4189–99. http://dx.doi.org/10.3233/jifs-169977.

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49

ALVIANO, MARIO, and RAFAEL PEÑALOZA. "Fuzzy answer set computation via satisfiability modulo theories." Theory and Practice of Logic Programming 15, no. 4-5 (July 2015): 588–603. http://dx.doi.org/10.1017/s1471068415000241.

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AbstractFuzzy answer set programming (FASP) combines two declarative frameworks, answer set programming and fuzzy logic, in order to model reasoning by default over imprecise information. Several connectives are available to combine different expressions; in particular the Gödel and Łukasiewicz fuzzy connectives are usually considered, due to their properties. Although the Gödel conjunction can be easily eliminated from rule heads, we show through complexity arguments that such a simplification is infeasible in general for all other connectives. The paper analyzes a translation of FASP programs into satisfiability modulo theories (SMT), which in general produces quantified formulas because of the minimality of the semantics. Structural properties of many FASP programs allow to eliminate the quantification, or to sensibly reduce the number of quantified variables. Indeed, integrality constraints can replace recursive rules commonly used to force Boolean interpretations, and completion subformulas can guarantee minimality for acyclic programs with atomic heads. Moreover, head cycle free rules can be replaced by shifted subprograms, whose structure depends on the eliminated head connective, so that ordered completion may replace the minimality check if also Łukasiewicz disjunction in rule bodies is acyclic. The paper also presents and evaluates a prototype system implementing these translations.
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50

Wang, Ning, Marek Reformat, Wen Yao, Yong Zhao, and Xiaoqian Chen. "Fuzzy Linear regression based on approximate Bayesian computation." Applied Soft Computing 97 (December 2020): 106763. http://dx.doi.org/10.1016/j.asoc.2020.106763.

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