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Journal articles on the topic 'Fuzzy inference systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Terenchuk, Svitlana, Yuliia Riabchun, and Maksym Delembovskyi. "IDENTIFICATION OF ENTRANT’S ABILITIES ON THE BASIS OF SUGENO-TYPE FUZZY INFERENCE SYSTEMS." Aviation 26, no. 4 (2022): 176–82. http://dx.doi.org/10.3846/aviation.2022.17636.

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In the conditions of effective training in aviation for dispatchers and pilots, it requires the use of infocommunication systems capable of working under conditions of fuzzy uncertainty in real time. The functioning of such systems is based on fuzzy inference systems. However, the development and implementation of these systems requires the creation of fuzzy knowledge bases. Therefore, special attention in this study is paid to the creation of a system of fuzzy inferences and the formation of a fuzzy knowledge base of this system. The result is a lozenge-type fuzzy inference system. The fuzzy

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Romanov, Anton, Julia Stroeva, Aleksey Filippov, and Nadezhda Yarushkina. "An Approach to Building Decision Support Systems Based on an Ontology Service." Mathematics 9, no. 22 (2021): 2946. http://dx.doi.org/10.3390/math9222946.

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Modern decision support systems (DSSs) need components for storing knowledge. Moreover, DSSs must support fuzzy inference to work with uncertainty. Ontologies are designed to represent knowledge of complex structures and to perform inference tasks. Developers must use the OWLAPI and SWRL API libraries to use ontology features. They are impossible to use in DSSs written in programming languages not for Java Virtual Machines. The FuzzyOWL library and the FuzzyDL inference engine are required to work with fuzzy ontologies. The FuzzyOWL library is currently unmaintained and does not have a public

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3

Mazandarani, Mehran, and Xiu Li. "Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems." IEEE Access 8 (2020): 126066–82. http://dx.doi.org/10.1109/access.2020.3008064.

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4

Kong, A. "Sparse distributed fuzzy inference systems." Soft Computing 10, no. 7 (2005): 567–77. http://dx.doi.org/10.1007/s00500-005-0518-4.

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5

Provotar, O. I., and O. O. Provotar. "Credibility in Fuzzy Inference Systems." Cybernetics and Systems Analysis 53, no. 6 (2017): 866–75. http://dx.doi.org/10.1007/s10559-017-9988-5.

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6

Kerk, Yi Wen, Kai Meng Tay, and Chee Peng Lim. "Monotone Interval Fuzzy Inference Systems." IEEE Transactions on Fuzzy Systems 27, no. 11 (2019): 2255–64. http://dx.doi.org/10.1109/tfuzz.2019.2896852.

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Mazandarani, Mehran, and Li Xiu. "Interval type-2 fractional fuzzy inference systems: Towards an evolution in fuzzy inference systems." Expert Systems with Applications 189 (March 2022): 115947. http://dx.doi.org/10.1016/j.eswa.2021.115947.

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Jacquin, Alexandra P., and Asaad Y. Shamseldin. "Review of the application of fuzzy inference systems in river flow forecasting." Journal of Hydroinformatics 11, no. 3-4 (2009): 202–10. http://dx.doi.org/10.2166/hydro.2009.038.

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This paper provides a general overview about the use of fuzzy inference systems in the important field of river flow forecasting. It discusses the overall operation of the main two types of fuzzy inference systems, namely Mamdani and Takagi–Sugeno–Kang fuzzy inference systems, and the critical issues related to their application. A literature review of existing studies dealing with the use of fuzzy inference systems in river flow forecasting models is presented, followed by some recommendations for future research areas. This review shows that fuzzy inference systems can be used as effective t

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Díaz-Montarroso, Carolina, Nicolás Madrid, and Eloísa Ramírez-Poussa. "Correctness of Fuzzy Inference Systems Based on f-Inclusion." Mathematics 13, no. 11 (2025): 1897. https://doi.org/10.3390/math13111897.

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Recent work has shown that the f-index of inclusion can serve as a foundation for modeling Generalized Modus Ponens. In this paper, we develop a novel fuzzy inference system based on this inference rule. To establish its soundness, we connect it to a Fuzzy Description Logic LU enriched with fuzzy modifiers (also known as fuzzy hedges). This logic background provides to the approach a strength absent in most fuzzy inference systems in the literature, which allows us to formally prove a series of results that culminate in a final correctness theorem for the proposed fuzzy inference system. This

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Virgil Negoita, Constantin. "Neural Networks as Fuzzy Systems." Kybernetes 23, no. 3 (1994): 7–9. http://dx.doi.org/10.1108/03684929410059000.

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Any fuzzy system is a knowledge‐based system which implies an inference engine. Proposes neural networks as a means of performing the inference. Using the Theorem of Representation proposes an encoding scheme that allows the neural network to be trained to perform modus ponens.

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11

Neshat, Mehdi, Ali Adeli, Ghodrat Sepidnam, and Mehdi Sargolzaei. "Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems." International Journal of Advanced Manufacturing Technology 63, no. 1-4 (2012): 373–90. http://dx.doi.org/10.1007/s00170-012-3914-9.

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Melin, Patricia, Daniela Sánchez, and Oscar Castillo. "Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms." Axioms 13, no. 1 (2023): 5. http://dx.doi.org/10.3390/axioms13010005.

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An essential aspect of healthcare is receiving an appropriate and opportune disease diagnosis. In recent years, there has been enormous progress in combining artificial intelligence to help professionals perform these tasks. The design of interval Type-3 fuzzy inference systems (IT3FIS) for medical classification is proposed in this work. This work proposed a genetic algorithm (GA) for the IT3FIS design where the fuzzy inputs correspond to attributes relational to a particular disease. This optimization allows us to find some main fuzzy inference systems (FIS) parameters, such as membership fu

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13

Wang, Yingxu. "Fuzzy Semantic Models of Fuzzy Concepts in Fuzzy Systems." International Journal of Fuzzy Systems and Advanced Applications 9 (March 13, 2022): 57–62. http://dx.doi.org/10.46300/91017.2022.9.9.

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The fuzzy properties of language semantics are a central problem towards machine-enabled natural language processing in cognitive linguistics, fuzzy systems, and computational linguistics. A formal method for rigorously describing and manipulating fuzzy semantics is sought for bridging the gap between humans and cognitive fuzzy systems. The mathematical model of fuzzy concepts is rigorously described as a hyperstructure of fuzzy sets of attributes, objects, relations, and qualifications, which serves as the basic unit of fuzzy semantics for denoting languages entities in semantic analyses. The

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14

LEE, KEON-MYUNG, and HYUNG LEE-KWANG. "FUZZY INFORMATION PROCESSING FOR EXPERT SYSTEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 01 (1995): 93–109. http://dx.doi.org/10.1142/s0218488595000098.

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This paper investigates the problems incurred when fuzzy values and certainty factors are used in rule-based knowledge representation. It proposes several measures for evaluating the satisfaction degree of fuzzy matching, fuzzy comparison and interval inclusion occurring in the course of inference for such knowledge representation. It introduces an inference method for such knowledge representation. In addition, it suggests a strategy for flexibly using and managing both conventional rules and fuzzy production rules in rule-based systems. Finally a fuzzy expert system shell, called FOPS5, desi

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Mudia, Halim. "Comparative Study of Mamdani-type and Sugeno-type Fuzzy Inference Systems for Coupled Water Tank." Indonesian Journal of Artificial Intelligence and Data Mining 3, no. 1 (2020): 42. http://dx.doi.org/10.24014/ijaidm.v3i1.9309.

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The level and flow control in tanks are the heart of all chemical engineering system. The control of liquid level in tanks and flow between tanks is a basic problem in the process industries. Many times the liquids will be processed by chemical or mixing treatment in the tanks, but always the level of fluid in the tanks must be controlled and the flow between tanks must be regulated in presence of non-linearity. Threfore, in this paper will use fuzzy inference systems to control of level 2 are developed using Mamdani-type and Sugeno-type fuzzy models. The outcome obtained by two fuzzy inferenc

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Titov, Andrei P. "SOFTWARE IMPLEMENTATION OF THE CO-ACTIVE NEURO-FUZZY INFERENCE SYSTEM." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 2 (2024): 26–43. http://dx.doi.org/10.28995/2686-679x-2024-2-26-43.

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The article deals with the implementation of a neural network with fuzzy logic based on the Co-Active Neuro-Fuzzy Inference System (CANFIS) model. The CANFIS model is an adaptive neuro-fuzzy system that combines neural networks and fuzzy logic for processing data with uncertainty and fuzziness. CANFIS uses fuzzy rules and output mechanisms to convert input data into output values. It consists of several layers, including an input layer, hidden layers and an output layer, where each layer contains neurons performing fuzzy activation and output of results. The relevance of the work lies in the f

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Mencar, Corrado, Giovanna Castellano, and Anna M. Fanelli. "Interface optimality in fuzzy inference systems." International Journal of Approximate Reasoning 41, no. 2 (2006): 128–45. http://dx.doi.org/10.1016/j.ijar.2005.06.013.

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Provotar, A. I., A. V. Lapko, and A. A. Provotar. "Fuzzy inference systems and their applications." Cybernetics and Systems Analysis 49, no. 4 (2013): 517–25. http://dx.doi.org/10.1007/s10559-013-9537-9.

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MARQUES PEREIRA, R. A., R. A. RIBEIRO, and P. SERRA. "RULE CORRELATION AND CHOQUET INTEGRATION IN FUZZY INFERENCE SYSTEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 05 (2008): 601–26. http://dx.doi.org/10.1142/s0218488508005522.

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We propose an extension of the Takagi-Sugeno-Kang (TSK) fuzzy inference system, using Choquet integration for aggregating the single rule outputs. In the new Choquet-TSK fuzzy inference system, the pairwise synergies between rules are encoded in a rule correlation matrix computed from the activation pattern of the rule base. The rule correlation matrix is then used to modulate the parameters of the Choquet integration scheme in order to compensate for the effect of rule synergies, which are present in most rule bases to a higher or lesser extent.The standard TSK fuzzy inference system remains

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20

LUI, HO CHUNG. "ADAPTIVE TRUTH VALUED FLOW INFERENCE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 03 (1994): 279–86. http://dx.doi.org/10.1142/s0218488594000225.

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Based on the Truth Value Flow Inference(TVFI) theory presented by P.Z.Wang, a fuzzy rule P → Q can be denoted as a fuzzy point in the X × Y domain, when a set of rules are given, the fuzzy points are joined together to become a "fuzzy mountain". Such mountain represents the causal relationship between X and Y. This paper describes an adaptation procedure so that the fuzzy mountain can be refined from past examples. Experimental result shows that after learning, the fuzzy relation approaches to the true but unknown causal mapping between X and Y.

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21

Makhortov, S. D. "On the Solvability and Number of Solutions of Production-Logical Equations in a Fuzzy LP-Structure." Programmnaya Ingeneria 12, no. 1 (2021): 40–47. http://dx.doi.org/10.17587/prin.12.40-47.

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For the construction and study of formal models of intelligent information systems, algebraic methods are useful. One of the topical directions here is the production-type logical systems, which are widespread in computer science. In recent years, the author and his followers have been developing the algebraic theory of LP-structures (lattice production structures). It is designed to formalize and solve a number of knowledge management problems in production systems. The method of relevant backward inference (LP-inference) was also introduced and investigated, which significantly reduces the n

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22

Crockett, Keeley A., Zuhair Bandar, Jay Fowdar, and James O'Shea. "Genetic tuning of fuzzy inference within fuzzy classifier systems." Expert Systems 23, no. 2 (2006): 63–82. http://dx.doi.org/10.1111/j.1468-0394.2006.00325.x.

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23

Anikin, lgor, and lgor Zinoviev. "New Type of Takagi-Sugeno Fuzzy Inference System as Universal Approximator." Applied Mechanics and Materials 598 (July 2014): 453–58. http://dx.doi.org/10.4028/www.scientific.net/amm.598.453.

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A new type of fuzzy inference systems (FIS) is presenting. It is based on Takagi-Sugeno fuzzy inference system. New FIS has been called the enhanced fuzzy regression (EFR). In opposition to the Takagi-Sugeno, new type of FIS has fuzzy coefficients in right parts of the fuzzy rules. Fuzzy approximation theorem has been proved for the EFR. We have suggested learning procedure for EFR inference system.

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24

Su, Pan, and Xueying Ren. "Fuzzy Rule Interpolation Methods Based on Sparse Rule Bases." International Journal of Computer Science and Information Technology 5, no. 3 (2025): 83–91. https://doi.org/10.62051/ijcsit.v5n3.08.

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Fuzzy rule interpolation algorithms have broad applications in computational fuzzy inference systems. This paper systematically introduces interpolation methods based on α-cuts. It focuses on two classical α-cut-based interpolation methods: the KH fuzzy rule interpolation method and the Lagrange fuzzy rule interpolation method. Through theoretical analysis and comparative studies, the fundamental principles, performance characteristics, and limitations of these two interpolation algorithms are explored in depth. Based on this, a fuzzy inference system for the "tip calculation problem" was cons

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25

ZENG, WENYI, and HONGXING LI. "INNER PRODUCT TRUTH-VALUED FLOW INFERENCE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13, no. 06 (2005): 601–12. http://dx.doi.org/10.1142/s0218488505003692.

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Inference problems are one of the main research topics in the artificial intellect field. So far there have been various inference systems, some of them have been applied in fuzzy control according to their feature. In 1989, the concept of truth-valued flow inference was introduced by Wang1, and its mathematical theory of truth-valued flow inference was set up by Wang2 in 1995. In this paper, aimed at the real meaning of the truth-valued in the truth-valued flow inference, we introduce the concepts of the inner product truth-valued and inner product truth-valued flow inference, and analyze som

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26

DEL CAMPO, INÉS, JAVIER ECHANOBE, KOLDO BASTERRETXEA, and GUILLERMO BOSQUE. "SCALABLE ARCHITECTURE FOR HIGH-SPEED MULTIDIMENSIONAL FUZZY INFERENCE SYSTEMS." Journal of Circuits, Systems and Computers 20, no. 03 (2011): 375–400. http://dx.doi.org/10.1142/s0218126611007359.

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This paper presents a scalable architecture suitable for the implementation of high-speed fuzzy inference systems on reconfigurable hardware. The main features of the proposed architecture, based on the Takagi–Sugeno inference model, are scalability, high performance, and flexibility. A scalable fuzzy inference system (FIS) must be efficient and practical when applied to complex situations, such as multidimensional problems with a large number of membership functions and a large rule base. Several current application areas of fuzzy computation require such enhanced capabilities to deal with re

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Castillo, Oscar, Patricia Melin, Fevrier Valdez, et al. "Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution Algorithms." Algorithms 12, no. 1 (2019): 17. http://dx.doi.org/10.3390/a12010017.

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Nowadays, dynamic parameter adaptation has been shown to provide a significant improvement in several metaheuristic optimization methods, and one of the main ways to realize this dynamic adaptation is the implementation of Fuzzy Inference Systems. The main reason for this is because Fuzzy Inference Systems can be designed based on human knowledge, and this can provide an intelligent dynamic adaptation of parameters in metaheuristics. In addition, with the coming forth of Type-2 Fuzzy Logic, the capability of uncertainty handling offers an attractive improvement for dynamic parameter adaptation

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Ziyadullaev, Davron, Dilnoz Muhamediyeva, Zafar Abdullaev, Sharofiddin Aynaqulov, and Khasanturdi Kayumov. "Generalized models of a production system of fuzzy conclusion." E3S Web of Conferences 365 (2023): 01019. http://dx.doi.org/10.1051/e3sconf/202336501019.

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The aim of the research is to study the models, rules, and fuzzy inference engines, which occupy the main place in the knowledgebase, and models of the logic inference engines and simulation modeling, focused on supporting the adoption of semi-structured decisions under uncertainty. This implies the relevance of the task of developing theoretical and methodological tools that provide automation of the processes of fuzzy inference systems. Research methods are the theory of fuzzy sets and fuzzy logic. New scientific results are the design and formation of a set of production rules from a given

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ZANCHETTIN, CLEBER, LEANDRO L. MINKU, and TERESA B. LUDERMIR. "DESIGN OF EXPERIMENTS IN NEURO-FUZZY SYSTEMS." International Journal of Computational Intelligence and Applications 09, no. 02 (2010): 137–52. http://dx.doi.org/10.1142/s1469026810002823.

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Interest in hybrid methods that combine artificial neural networks and fuzzy inference systems has grown in recent years. These systems are robust solutions that search for representations of domain knowledge, reasoning on uncertainty, automatic learning and adaptation. However, the design and definition of the parameter effectiveness of such systems is still a hard task. In the present work, we perform a statistical analysis to verify interactions and interrelations between parameters in the design of neuro-fuzzy systems. The analysis is carried out using a powerful statistical tool, namely,

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Gennady, Kaniuk, Vasylets Tetiana, Varfolomiyev Oleksiy, Mezerya Andrey, and Antonenko Nataliia. "Development of neuralnetwork and fuzzy models of multimass electromechanical systems." Eastern-European Journal of Enterprise Technologies 3, no. 2(99) (2019): 51–63. https://doi.org/10.15587/1729-4061.2019.169080.

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The study objective was to construct models of multimass electromechanical systems using neural nets, fuzzy inference systems and hybrid networks by means of MATLAB tools. A model of a system in a form of a neural net or a neuro-fuzzy inference system was constructed on the basis of known input signals and signals measured at the system output. Methods of the theory of artificial neural nets and methods of the fuzzy modeling technology were used in the study. A neural net for solving the problem of identification of the electromechanical systems with complex kinematic connections was synthesiz

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Barraza, Juan, Patricia Melin, Fevrier Valdez, and Claudia I. Gonzalez. "Modeling of Fuzzy Systems Based on the Competitive Neural Network." Applied Sciences 13, no. 24 (2023): 13091. http://dx.doi.org/10.3390/app132413091.

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This paper presents a method to dynamically model Type-1 fuzzy inference systems using a Competitive Neural Network. The aim is to exploit the potential of Competitive Neural Networks and fuzzy logic systems to generate an intelligent hybrid model with the ability to group and classify any dataset. The approach uses the Competitive Neural Network to cluster the dataset and the fuzzy model to perform the classification. It is important to note that the fuzzy inference system is generated automatically from the classes and centroids obtained with the Competitive Neural Network, namely, all the p

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Huong, Trieu Thu, Chu Thi Hong Hai, and Phan Thanh Duc. "An extension of complex fuzzy inference system for alert earlier credit risk at business banks." Edelweiss Applied Science and Technology 9, no. 4 (2025): 2147–56. https://doi.org/10.55214/25768484.v9i4.6500.

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The purpose of this study is to suggest a novel integrated model for assessing credit risk at commercial banks that is based on a complex fuzzy transfer learning framework. Research Design and Methodology: We used transfer learning on a complex fuzzy inference system, complex fuzzy set theory, and a complex fuzzy inference system to build a credit risk prediction model. Parallel to this, we compared the proposed model with the previously used credit risk prediction method known as the Mamdani CFIS model. Results: The study has validated the complex fuzzy inference model's capacity to accuratel

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33

Uehara, Kiyohiko. "Special Issue on Advances in Fuzzy Inference and its Related Techniques." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 1 (2013): 43. http://dx.doi.org/10.20965/jaciii.2013.p0043.

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Fuzzy inference provides a way to describe system behavior using humanly understandable rules. Based on this advantage, fuzzy inference has been applied in a wide variety of fields, including control, prediction, and pattern recognition. It has also had a corresponding impact on industrial applications. The four articles included in this special issue cover the advances made in fuzzy inference and related techniques. The first paper proposes a method for fuzzy rule interpolation on the basis of the generalized mean. This method makes it possible to perform nonlinear mapping of convex fuzzy set

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34

Seki, Hirosato, and Kai Meng Tay. "On the Monotonicity of Fuzzy Inference Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 5 (2012): 592–602. http://dx.doi.org/10.20965/jaciii.2012.p0592.

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Monotonicity property is very important in real systems. The monotonicity may need to be satisfied in a variety of application domains, e.g., control, medical diagnosis, educational evaluation, etc. A search in the literature reveals that the importance of the monotonicity in fuzzy inference system has been highlighted. Therefore, this paper surveys the works relating the monotonicity for various fuzzy inference systems. It firstly focuses on the monotonicity of the Mamdani inference model. Themonotonicity ofMamdani model is shown by using a defuzzification method in cases of three t-norms. Se

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35

Lynn Yaling Cai and Hon Keung Kwan. "Fuzzy classifications using fuzzy inference networks." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 28, no. 3 (1998): 334–47. http://dx.doi.org/10.1109/3477.678627.

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Yershov, S. V., and R. М. Ponomarenko. "Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems." PROBLEMS IN PROGRAMMING, no. 2-3 (June 2016): 141–49. http://dx.doi.org/10.15407/pp2016.02-03.141.

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Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models

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37

Alhaqbani, Bandar, and Colin Fidge. "Probabilistic Inference Channel Detection and Restriction Applied to Patients’ Privacy Assurance." International Journal of Information Security and Privacy 4, no. 4 (2010): 35–59. http://dx.doi.org/10.4018/jisp.2010100103.

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Traditional access control models protect sensitive data from unauthorised direct accesses; however, they fail to prevent indirect inferences. Information disclosure via inference channels occurs when secret information is derived from unclassified (non-secure) information and other sources like metadata and public observations. Previously, techniques using precise and fuzzy functional dependencies were proposed to detect inference channels. However, such methods are inappropriate when probabilistic relationships exist among data items that may be used to infer information with a predictable l

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Ahmad, N., M. Hanmandlu, and M. F. Azeem. "Generalization of adaptive neuro-fuzzy inference systems." IEEE Transactions on Neural Networks 11, no. 6 (2000): 1332–46. http://dx.doi.org/10.1109/72.883438.

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Lotfi, A., H. C. Andersen, and Ah Chung Tsoi. "Interpretation preservation of adaptive fuzzy inference systems." International Journal of Approximate Reasoning 15, no. 4 (1996): 379–94. http://dx.doi.org/10.1016/s0888-613x(96)00070-9.

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40

Štěpnička, Martin, and Sayantan Mandal. "Fuzzy inference systems preserving Moser–Navara axioms." Fuzzy Sets and Systems 338 (May 2018): 97–116. http://dx.doi.org/10.1016/j.fss.2017.11.005.

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Guillaume, Serge, and Brigitte Charnomordic. "Learning interpretable fuzzy inference systems with FisPro." Information Sciences 181, no. 20 (2011): 4409–27. http://dx.doi.org/10.1016/j.ins.2011.03.025.

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Sala, A., and P. Albertos. "Fuzzy systems evaluation: The inference error approach." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 28, no. 2 (1998): 268–75. http://dx.doi.org/10.1109/3477.662768.

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Golosovskiy, M. S., A. V. Bogomolov, and M. E. Balandov. "Optimized Fuzzy Inference for Sugeno-Type Systems." Automatic Documentation and Mathematical Linguistics 56, no. 5 (2022): 237–44. http://dx.doi.org/10.3103/s0005105522050028.

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Ontiveros-Robles, Emanuel, Oscar Castillo, and Patricia Melin. "An approach for non-singleton generalized Type-2 fuzzy classifiers." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7203–15. http://dx.doi.org/10.3233/jifs-200639.

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In recent years, successful applications of singleton fuzzy inference systems have been made in a plethora of different kinds of problems, for example in the areas of control, digital image processing, time series prediction, fault detection and classification. However, there exists another relatively less explored approach, which is the use of non-singleton fuzzy inference systems. This approach offers an interesting way for handling uncertainty in complex problems by considering inputs with uncertainty, while the conventional Fuzzy Systems have their inputs with crisp values (singleton syste

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45

Yubazaki, Naoyoshi, Jianqiang Yi, and Kaoru Hirota. "SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 1 (1997): 23–30. http://dx.doi.org/10.20965/jaciii.1997.p0023.

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A new fuzzy inference model, SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model, is proposed for plural input fuzzy control. For each input item, an importance degree is defined and single input fuzzy rule module is constructed. The importance degrees control the roles of the input items in systems. The model output is obtained by the summation of the products of the importance degree and the fuzzy inference result of each SIRM. The proposed model needs both very few rules and parameters, and the rules can be designed much easier. The new model is first applied to typical second

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DEMİRKAN PİŞKİN, Minel, and Eren BAŞ. "Forecasting Monthly Housing Sales to Foreigners with Type 1 Fuzzy Regression Functions Approach Based on Ridge Regression." Karadeniz Fen Bilimleri Dergisi 12, no. 2 (2022): 571–83. http://dx.doi.org/10.31466/kfbd.1074832.

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Artificial neural networks, fuzzy inference systems, and hybrid methods where these methods are used together have been frequently used in forecasting problems. Although fuzzy inference systems produce very effective results in forecasting problems, the fact that many classical fuzzy inference systems depend on the rule base makes it difficult to implement these methods. The type 1 fuzzy regression functions approach, which is not dependent on the rule base and has a simpler structure than many fuzzy inference systems, is frequently used in forecasting problems. Although the Type 1 fuzzy regre

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Makhortov, S. D. "Methods for Solving Production-Logical Equations in a Fuzzy LP-Structure." Programmnaya Ingeneria 11, no. 6 (2020): 342–48. http://dx.doi.org/10.17587/prin.11.342-348.

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Algebraic methods provide an effective formalism for constructing and researching models of a wide range of information systems, especially intelligent ones. This provision fully applies to the production-type logical systems widespread in computer science. In the last decade, the author has created an algebraic theory of LP-structures (lattice production structures), which makes it possible to effectively solve a number of important problems related to production systems. Such tasks include equivalent transformations, verification, minimization of knowledge bases, and acceleration of logical

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Ponce, Hiram, Pedro Ponce, and Arturo Molina. "Artificial Hydrocarbon Networks Fuzzy Inference System." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/531031.

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This paper presents a novel fuzzy inference model based on artificial hydrocarbon networks, a computational algorithm for modeling problems based on chemical hydrocarbon compounds. In particular, the proposed fuzzy-molecular inference model (FIM-model) uses molecular units of information to partition the output space in the defuzzification step. Moreover, these molecules are linguistic units that can be partially understandable due to the organized structure of the topology and metadata parameters involved in artificial hydrocarbon networks. In addition, a position controller for a direct curr

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Stamou, G. B., and S. G. Tzafestas. "Fuzzy relation equations and fuzzy inference systems: an inside approach." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 29, no. 6 (1999): 694–702. http://dx.doi.org/10.1109/3477.809025.

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Sun, Hua, and Li Li. "A Trustable Approach Based on Fuzzy Inference for P2P Systems." Applied Mechanics and Materials 121-126 (October 2011): 3914–18. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.3914.

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Trust management is one of the key problems in the P2P systems and e-commerce. Before they have the transaction, people always want to know whether the other side of the transaction partners can be trusted and how much degree of their trustworthiness. This paper proposes a trustable approach based on fuzzy inference in P2P systems and gives a detail discussion of fuzzy inference. They can inference their trust against the trustworthiness of the rules and compute the trustworthiness of the conclusion. This approach is adapted to the P2P environment which the rule and the proof which may be show

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