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Multiplicative Integral Theory of Generalized Orthopair fuzzy sets and Its Applications

Journal of Systems Science and Systems Engineering 2022年第4期31卷 457-479页

作者： Jie Gao Zeshui Xu Chao Zhong Yunshu Mao School of Business Administration Southwestern University of Finance and EconomicsChengdu610207China Business School Sichuan UniversityChengdu610064China

There are two main issues of fuzzy multi-attribute decision-making:determine the weight of each attribute and choose an appropriate aggregation method to integrate the evaluation information of different attributes.In order to solve the multi-attribute decision-making problem in generalized orthopair fuzzy environment with unknown attribute weights more effectively,we give a decision-making method based on generalized orthopair fuzzy definite integrals.To be specific,we first introduce the complement operations of q-rung orthopair fuzzy numbers,and then investigate the multiplicative q-rung orthopair fuzzy calculus.Through the complement operations,we establish the mutual conversion formula between additive and multiplicative q-rung orthopair fuzzy calculus theory.Then,we give a multiplicative integral-based q-rung orthopair fuzzy multi-attribute decision-making method,and discuss the relationship between the q-rung orthopair fuzzy definite integrals and the q-rung orthopair fuzzy weighted geometric operator.Compared with traditional decision-making methods,this method does not rely on subjective weight information,which is especially important when dealing with large sample data.Finally,the application of election is studied to verify the feasibility and effectiveness of the proposed method.With the introduction of generalized orthopair fuzzy sets,the expression form of election evaluation information has been expanded.We also provide some examples to compare the obtained results with the results generated by the addition operation and reveal the correlation between them.

关键词： fuzzy sets decision making aggregation operators

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fuzzy sets Assisted Forthcoming Event Detection from Distributed Sensory Data

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Chinese Journal of Electronics 2017年第6期26卷 1308-1314页

作者： LI Yinglong MO Shangfeng LI Yanyan ZHOU Zhiqiang LYU Mingqi College of Computer Science and Technology Zhejiang University of Technology Hunan University of Science and Technology

Detecting the forthcoming events timely is a primary way to minimize their(events) damages in distributed sensor systems. We propose a novel event detection framework for sensor networks with multi microenvironments using fuzzy sets. fuzzy sets assisted event information description and fusion approaches are proposed,as well as two distributed Node-level forthcoming event detection(NFED) algorithms are devised respectively. Experimental results on both real-life and synthetic data sets demonstrate that our NFED-by leveraging Spatial correlations(NFED-by SC) algorithm only requires a small amount of data transmission with accuracy guarantee.

关键词： Event detection fuzzy sets Energy efficiency Accuracy Sensor network

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New Time-varying fuzzy sets Based on a PSO Midpoint of the Universe of Discourse

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International Journal of Automation and computing 2016年第4期13卷 392-400页

作者： Salim Ziani Automatic and Robotics Laboratory Department of Electronics University of Constantine 1 Constantine 25000 Algeria

The paper presents a robust parallel distributed compensation （PDC） fuzzy controller for a nonlinear and certain system in continuous time described by the Tal^gi-Sugeno （T-S） fuzzy model. This controller is based on a new type of time-varying fuzzy sets （TVFS）. These fuzzy sets are characterized by displacement of the kernels to the right or left of the universe of discourse, and they are directed by a well-defined criterion. In this work, we only focused on the movement of midpoint of the universe. The movements of this midpoint are optimized by particle swarm optimization （PSO） approach.

关键词： fuzzy sets fuzzy system membership function parallel distributed compensation （PDC） fuzzy controller particle swarmoptimization （PSO） approach linear matrix inequality （LMI）.

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APPLICATION OF METHOD OF fuzzy sets TO THE ANALYSIS OF WATER MASSES IN THE NORTHERN SOUTH CHINA SEA

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中国海洋大学学报(自然科学版) 1989年第S1期 474-492页

作者： Li Fengqi, Su Yusong and Fan Liqun(Received December 5, 1986 accepted March 15, 1987)

Some methods of fuzzy sets are applied to the analysis of water masses in the north area of the South China Sea. In distinguishing water masses with the fuzzy clustering method, the authors propose a fuzzy similar relation based on Euclidean distance, and suggest a method for determining the number of water masses by the F-test, and further adjust rationally the boundaries of water masses and the membership relation of samples by the elastic classification method. Eight water masses are distinguished in the area, i.e., Nearshore Diluted Water F, Nearshore Mixed Water M, Warm Surface Water WS, Surface Water S, Surface-Subsurface Mixed Water SU, Subsurface Water U, Subsurface- Intermediate Mixed Water UI and Intermediate Water I. These point-sets of various water masses on the θ-S diagram may be summed up in three types, that is, the deformed elliptical convex point-set, the half-boundless convex point-set and the point-set with single-side concave. For them, we have proposed methods of making their memb

关键词： water mass the South China Sea fuzzy sets the membership function the lattice approach degree the fuzzy entropy

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fuzzy-based indoor scene modeling with differentiated examples

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Computational Visual Media 2023年第4期9卷 717-732页

作者： Qiang Fu Shuhan He Hongbo Fu Xueming Li Zhigang Deng School of Digital Media and Design Arts Beijing University of Posts and TelecommunicationsBeijingChina School of Creative Media City University of Hong KongHong KongChina Department of Computer Science University of HoustonTXUSA

Well-designed indoor scenes incorporate interior design knowledge,which has been an essential prior for most indoor scene modeling methods.However,the layout qualities of indoor scene datasets are often uneven,and most existing data-driven methods do not differentiate indoor scene examples in terms of quality.In this work,we aim to explore an approach that leverages datasets with differentiated indoor scene examples for indoor scene modeling.Our solution conducts subjective evaluations on lightweight datasets having various room configurations and furniture layouts,via pairwise comparisons based on fuzzy set theory.We also develop a system to use such examples to guide indoor scene modeling using user-specified objects.Specifically,we focus on object groups associated with certain human activities,and define room features to encode the relations between the position and direction of an object group and the room configuration.To perform indoor scene modeling,given an empty room,our system first assesses it in terms of the user-specified object groups,and then places associated objects in the room guided by the assessment results.A series of experimental results and comparisons to state-of-the-art indoor scene synthesis methods are presented to validate the usefulness and effectiveness of our approach.

关键词： indoor scene modeling modeling by example fuzzy sets

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fuzzy-based metaheuristic algorithm for optimization of fuzzy controller:fault-tolerant control application

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International Journal of Intelligent Computing and Cybernetics 2022年第4期15卷 599-624页

作者： Himanshukumar Rajendrabhai Patel Department of Instrumentation and Control Engineering Faculty of TechnologyDharmsinh Desai UniversityNadiadIndia

Purpose-fuzzy-based metaheuristic algorithm is used to optimize the fuzzy controllers for the nonlinear level control system subject to uncertainty specially in the main actuator that has lost effectiveness(LOE).To optimize the fuzzy controller,type-1 harmonic search(HS)and interval type-2(HS)will be used.Design/methodology/approach-The type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for Fault-Tolerant Control(FTC)applications,and this research proposes a fuzzy-based HS metaheuristic method.The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has lost effectiveness(LOE)and also the same controller will be tested on DC motor angular position control with and without noise.Findings-The key contribution of this work is the discovery of the best approach for generating an optimal vector of values for the fuzzy controller’s membership function optimization.This is done in order to improve the controller’s performance,bringing the process value of the two-tank level control process closer to the target process value(set point).It is worth noting that the type-1 fuzzy controller that has been optimized is an interval type-2 fuzzy system,which can handle more uncertainty than a type-1 fuzzy system.Originality/value-The type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for FTC applications,and this research proposes a fuzzy-based HS metaheuristic method.The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has LOE will be tested on DC motor angular position control with noise.Two nonlinear uncertain processes are used to demonstrate the effectiveness of the proposed control scheme.

关键词： Harmonic search algorithm fuzzy controller Type-2 fuzzy systems fuzzy sets

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Fusion of Infrared and Visible Images Using fuzzy Based Siamese Convolutional Network

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Computers, Materials & Continua 2022年第3期70卷 5503-5518页

作者： Kanika Bhalla Deepika Koundal Surbhi Bhatia Mohammad Khalid Imam Rahmani Muhammad Tahir Department of Electrical Engineering National Taipei University of TechnologyTaipei10608Taiwan Department Virtualization School of Computer ScienceUniversity of Petroleum&Energy StudiesDehradunIndia College of Computer Science and Information technology King Faisal University36362Saudi Arabia College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia

Traditional techniques based on image fusion are arduous in integrating complementary or heterogeneous infrared(IR)/visible(VS)images.Dissimilarities in various kind of features in these images are vital to preserve in the single fused image.Hence,simultaneous preservation of both the aspects at the same time is a challenging task.However,most of the existing methods utilize the manual extraction of features;and manual complicated designing of fusion rules resulted in a blurry artifact in the fused image.Therefore,this study has proposed a hybrid algorithm for the integration of multi-features among two heterogeneous images.Firstly,fuzzification of two IR/VS images has been done by feeding it to the fuzzy sets to remove the uncertainty present in the background and object of interest of the image.Secondly,images have been learned by two parallel branches of the siamese convolutional neural network(CNN)to extract prominent features from the images as well as high-frequency information to produce focus maps containing source image information.Finally,the obtained focused maps which contained the detailed integrated information are directly mapped with the source image via pixelwise strategy to result in fused image.Different parameters have been used to evaluate the performance of the proposed image fusion by achieving 1.008 for mutual information(MI),0.841 for entropy(EG),0.655 for edge information(EI),0.652 for human perception(HP),and 0.980 for image structural similarity(ISS).Experimental results have shown that the proposed technique has attained the best qualitative and quantitative results using 78 publically available images in comparison to the existing discrete cosine transform(DCT),anisotropic diffusion&karhunen-loeve(ADKL),guided filter(GF),random walk(RW),principal component analysis(PCA),and convolutional neural network(CNN)methods.

关键词： Convolutional neural network fuzzy sets infrared and visible image fusion deep learning

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A novel extended Portuguese of Interactive and Multi-Criteria Decision Making and Archimedean Bonferroni mean operators based on prospect theory to select green supplier with complex q-rung orthopair fuzzy information

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 177-191页

作者： Zeeshan Ali Tahir Mahmood Jeonghwan Gwak Naeem Jan Department of Mathematics&Statistics International Islamic University IslamabadIslamabadPakistan Department of Software Korea National University of TransportationChungjuKorea Department of Biomedical Engineering Korea National University of TransportationChungjuKorea Department of AI Robotics Engineering Korea National University of TransportationChungjuKorea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungjuKorea

The word sustainable or green supply chain refers to the concept of incorporating sustainable environmental procedures into the traditional supply chain.Green supply chain management gives a chance to revise procedures,materials and operational ideas.Choosing the fuzziness of assessing data and the spiritual situations of experts in the decision-making procedure are two important issues.The main contribution of this analysis is to derive the theory of Archimedean Bonferroni mean operator for complex qrung orthopair fuzzy(CQROF)information,called the CQROF Archimedean Bonferroni mean and CQROF weighted Archimedean Bonferroni mean operators which are very valuable,dominant and classical type of aggregation operators used for examining the interrelationship among the finite number of attributes in modern data fusion theory.Inspirational and well-used properties of the initiated theories are also diagnosed with some special cases.Additionally,the theory of extended TODIM tool using the prospect theory based on CQROF information was discovered,which play an essential and critical role in the environment of fuzzy set theory.Finally,a real life problem by computing a green supply chain management based on the initiated CQROF operators was evaluated and fully illustrating the feasibility and efficiency of the diagnosed work with the help of a comparison between existing and prevailing theories.

关键词： fuzzy set fuzzy sets

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A Grey Simulation-Based fuzzy Hierarchical Approach for Diagnosing Healthcare Service Quality

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Computers, Materials & Continua 2023年第2期74卷 3231-3248页

作者： Phi-Hung Nguyen Hong-Anh Pham Faculty of Business FPT UniversityHanoi100000Vietnam

This study aims to assess and rank the service quality of the healthcare system utilizing a fuzzy Analytical Hierarchical Process(fuzzy AHP)andGreyRelationalAnalysis(fuzzy GRA)technique.In this study,the six primary characteristics of healthcare service quality,comprising Tangibles(A),Healthcare Staff(B),Responsiveness(C),Relationships(D),Support Service(E),and Accessibility(F),are examined through a case study of 20 private and public hospitals in Hanoi,Vietnam.The weighting results of fuzzy AHP technique indicated that Responsiveness(C)has the highest ranking,followed by Relationships(D)and Healthcare Staff(B).Meanwhile,Tangibility has finally comprised the next priority for increasing satisfaction with the service quality in the healthcare industry.Additionally,the highest service quality rankings are top 5 private hospitals via the fuzzy GRA approach.Notably,this proposed approach may be applied to a complex decision-making process,which often makes sense with subjective data or imprecise information.

关键词： MCDM fuzzy sets AHP GRA healthcare Vietnam

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Jensen-Renyi’s-Tsallis fuzzy Divergence Information Measure with its Applications

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Communications in Mathematics and Statistics 2022年第3期10卷 451-482页

作者： Ratika Kadian Satish Kumar Department of Mathematics Maharishi Markandeshwar(Deemed to be)UniversityMullana-Ambala 133207India

In this paper,we have characterized the sum of two general measures associated with two distributions with discrete random variables.One of these measures is logarithmic,while others contains the power of variables,named as joint representation of Renyi’s-Tsallis divergence measure.Then,we propose a divergence measure based on Jensen-Renyi’s-Tsallis entropy which is known as a Jensen-Renyi’s-Tsallis divergence measure.It is a generalization of J-divergence information measure.One of the silent features of this measure is that we can allot the equal weight to each probability distribution.This makes it specifically reasonable for the study of decision problems,where the weights could be the prior probabilities.Further,the idea has been generalized from probabilistic to fuzzy similarity/dissimilarity measure.Besides the validation of the proposed measure,some of its key properties are also studied.Further,the performance of the proposed measure is contrasted with some existing measures.At last,some illustrative examples are solved in the context of clustering analysis,financial diagnosis and pattern recognition which demonstrate the practicality and adequacy of the proposed measure between two fuzzy sets(FSs).

关键词： fuzzy sets Divergence measure Similarity measure Clustering analysis Financial diagnosis

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