Статті в журналах: "Fuzzy conclusion"

1

Кочеткова, Инесса, Inessa Kochetkova, Василий Рубанов, and Vasiliy Rubanov. "DEVELOPMENT OF DECISION-MAKING SIMULATOR BASED ON THEORY FUZZY SETS AND FUZZY CONCLUSION." Bulletin of Bryansk state technical university 2017, no. 2 (June 30, 2017): 217–23. http://dx.doi.org/10.12737/article_59353e2a224ed6.27761471.

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2

Pynko, Alexej P. "Fuzzy semantics for multiple-conclusion sequential calculi with structural rules." Fuzzy Sets and Systems 121, no. 3 (August 2001): 397–407. http://dx.doi.org/10.1016/s0165-0114(99)00152-9.

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3

ANDRECUT, M., and M. K. ALI. "FUZZY REINFORCEMENT LEARNING." International Journal of Modern Physics C 13, no. 05 (June 2002): 659–74. http://dx.doi.org/10.1142/s0129183102003450.

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Анотація:

Fuzzy logic represents an extension of classical logic, giving modes of approximate reasoning in an environment of uncertainty and imprecision. Fuzzy inference systems incorporates human knowledge into their knowledge base on the conclusions of the fuzzy rules, which are affected by subjective decisions. In this paper we show how the reinforcement learning technique can be used to tune the conclusion part of a fuzzy inference system. The fuzzy reinforcement learning technique is illustrated using two examples: the cart centering problem and the autonomous navigation problem.

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4

I, Siddikov, and D. Yadgarova. "DEVELOPMENT OF A HIGH-SPEED ALGORITM OF NEUROLOGICAL CONCLUSION." Technical science and innovation 2019, no. 1 (June 11, 2019): 154–60. http://dx.doi.org/10.51346/tstu-01.19.1.-77-0014.

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One of the ways to increase the efficiency of the process of managing continuous dynamic objects is to develop new or improve existing control systems based on modern methods involving the achievements of information technology. The article deals with the creation of highly efficient control algorithms for technological objects, operating in conditions of uncertainty, designed to manage real-life objects. An algorithm is proposed for the structural-parametric adaptation of the PID parameters (proportional-integral-differential) -regulator, which allows to reduce the number of iterations in the learning process of the fuzzy-logical inference algorithm by reducing empty solutions. To determine the empty solutions, hybrid algorithms are used, which include modernized genetic and immune algorithms, which in turn allow you to configure the adaptation parameters of artificial neural network models. A block diagram of an automated control system for executive mechanisms is proposed, which includes a block for adapting the correction of not only parameters, but also the structure of the control system, which allows to reduce the error in the results of training a neuro-fuzzy network from 8 to 1%. The proposed algorithm is simple to implement on microcontrollers, which allows it to be implemented in the tasks of process control in the conditions of information uncertainty in real conditions at the operation stage.

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5

WANG, SHYUE-LIANG, and TZUNG-PEI HONG. "INCOMPLETE INPUT INFERENCE ON FUZZY PRODUCTION SYSTEMS SUPPORTED BY PETRI NETS." International Journal on Artificial Intelligence Tools 09, no. 04 (December 2000): 537–49. http://dx.doi.org/10.1142/s0218213000000343.

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This paper proposes a new reasoning technique on fuzzy production systems while given input knowledge is incomplete. Based on the fuzzy Petri net formalism, the proposed algorithm can infer all possible conclusions and their corresponding missing inputs. The most possible conclusion can also be determined based on the criteria of the minimum number of missing inputs as well as the degree of truth of the conclusion. In addition, finiteness and computational complexity of the algorithm is investigated. As real decisions are typically made under incomplete input knowledge, this reasoning technique provides more realistic applications for fuzzy production systems.

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6

Chislov, O., N. Lyabakh, M. Kolesnikov, M. Bakalov, and D. Bezusov. "Fuzzy modelling of the transportation logistics processes." Journal of Physics: Conference Series 2131, no. 3 (December 1, 2021): 032007. http://dx.doi.org/10.1088/1742-6596/2131/3/032007.

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Abstract The relevance of mechanisms and fuzzy modeling methods used in transport and logistics processes is justified. The logic of the transport and logistics chains study by their decomposition into separate economic entities with subsequent synthesis, with taking into account their conflicting interests, is presented. The task of managing transport and logistics processes is set within the framework of the conceptual positions of the fuzzy sets theory: the use of linguistic variables, the concept of a fuzzy set, fuzzy inference, implemented on the basis of fuzzy sets operations. In this study, the fuzzy modeling on the bottom-up conclusions from the premises to the conclusion was used. Various methods of implication and defuzzification have been analyzed. An iterative procedure for managing transport and logistics chains and their links has been proposed, which ensures the adaptation of the process to the specified performance indicators. A calculated example is given for a marshalling yard as a link in the transport and logistics chain.

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7

Jin, Hyun-Soo. "A Method of Conclusion of Traffic Control Signal Proposal using Fuzzy Analytic Hierachy Process." Journal of the Korea Academia-Industrial cooperation Society 9, no. 6 (December 31, 2008): 1592–98. http://dx.doi.org/10.5762/kais.2008.9.6.1592.

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8

Pynko, Alexej P. "Not necessarily distributive fuzzy semantics for multiple-conclusion sequent calculi with weak structural rules." Fuzzy Sets and Systems 129, no. 2 (July 2002): 255–65. http://dx.doi.org/10.1016/s0165-0114(01)00164-6.

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9

Zhang, Xuefeng. "Comments on “Takagi–Sugeno fuzzy control for a wide class of fractional-order chaotic systems with uncertain parameters via linear matrix inequality”." Journal of Vibration and Control 26, no. 9-10 (November 28, 2019): 643–45. http://dx.doi.org/10.1177/1077546319889838.

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This article shows that sufficient conditions of Theorems 1–3 and the conclusions of Lemmas 1–2 for Takasi–Sugeno fuzzy model–based fractional order systems in the study “Takagi–Sugeno fuzzy control for a wide class of fractional order chaotic systems with uncertain parameters via linear matrix inequality” do not hold as asserted by the authors. The reason analysis is discussed in detail. Counterexamples are given to validate the conclusion.

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10

Jin, Shangzhu. "An Alternative Backward Fuzzy Rule Interpolation Method." International Journal of Software Science and Computational Intelligence 6, no. 4 (October 2014): 47–71. http://dx.doi.org/10.4018/ijssci.2014100104.

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Анотація:

Fuzzy set theory allows for the inclusion of vague human assessments in computing problems. Also, it provides an effective means for conflict resolution of multiple criteria and better assessment of options. Fuzzy rule interpolation offers a useful means for enhancing the robustness of fuzzy models by making inference possible in sparse rule-based systems. However, in real-world applications of inter-connected rule bases, situations may arise when certain crucial antecedents are absent from given observations. If such missing antecedents were involved in the subsequent interpolation process, the final conclusion would not be deducible using conventional means. To address this issue, an approach named backward fuzzy rule interpolation and extrapolation has been proposed recently, allowing the observations which directly relate to the conclusion to be inferred or interpolated from the known antecedents and conclusion. As such, it significantly extends the existing fuzzy rule interpolation techniques. However, the current idea has only been implemented via the use of the scale and move transformation-based fuzzy interpolation method, which utilise analogical reasoning mechanisms. In order to strengthen the versatility and feasibility of backward fuzzy interpolative reasoning, in this paper, an alternative a-cut-based interpolation method is proposed. Two numerical examples and comparative studies are provided in order to demonstrate the efficacy of the proposed work.

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11

PARK, BYOUNG-JUN, WITOLD PEDRYCZ, and SUNG-KWUN OH. "SIMPLIFIED FUZZY INFERENCE RULE-BASED GENETICALLY OPTIMIZED HYBRID FUZZY NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 02 (April 2008): 245–74. http://dx.doi.org/10.1142/s0218488508005169.

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In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the condition part of the rule-based structure of the gHFNN. The conclusion part of the gHFNN is designed using PNNs. We distinguish between two types of the simplified fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the conclusion part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, we experimented with three representative numerical examples. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when compared with other neurofuzzy models.

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12

ZHANG, JIA-LU, and HONG-JUN ZHOU. "FUZZY FILTERS ON THE RESIDUATED LATTICES." New Mathematics and Natural Computation 02, no. 01 (March 2006): 11–28. http://dx.doi.org/10.1142/s1793005706000373.

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In this paper, the lattice operations and the adjoint pair on the fuzzy filters set on residuated lattices are defined, the conclusion that the fuzzy filters lattice defined as such is a distributive residuated lattice is obtained. An order-reversing involution on the fuzzy strong-prime filters sublattice is introduced. It is proved that the fuzzy strong-prime filters sublattice is a quasi-Boolean algebra.

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13

BURGIN, MARK, and OKTAY DUMAN. "STATISTICAL FUZZY CONVERGENCE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 06 (December 2008): 879–902. http://dx.doi.org/10.1142/s0218488508005674.

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The goal of this work is the further development of neoclassical analysis, which extends the scope and results of the classical mathematical analysis by applying fuzzy logic to conventional mathematical objects, such as functions, sequences, and series. This allows us to reflect and model vagueness and uncertainty of our knowledge, which results from imprecision of measurement and inaccuracy of computation. Basing on the theory of fuzzy limits, we develop the structure of statistical fuzzy convergence and study its properties. Relations between statistical fuzzy convergence and fuzzy convergence are considered in the First Subsequence Theorem and the First Reduction Theorem. Algebraic structures of statistical fuzzy limits are described in the Linearity Theorem. Topological structures of statistical fuzzy limits are described in the Limit Set Theorem and Limit Fuzzy Set theorems. Relations between statistical convergence, statistical fuzzy convergence, ergodic systems, fuzzy convergence and convergence of statistical characteristics, such as the mean (average), and standard deviation, are studied in Secs. 2 and 4. Introduced constructions and obtained results open new directions for further research that are considered in the Conclusion.

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14

Dantsevich, Igor. "THE DEVELOPMENT SYSTEM INDISTINCT CONCLUSION ON THE BASIS REGULATOR POWER SHIP EQUIPMENT ON THE FUZZY LOGIC TECHNOLOGY OF MATLAB." University News. North-Caucasian Region. Technical Sciences Series, no. 1 (March 2016): 20–24. http://dx.doi.org/10.17213/0321-2653-2016-1-20-24.

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15

Makariadis, Stefanos, and Basil Papadopoulos. "Generalization of Fuzzy Connectives." Axioms 11, no. 3 (March 12, 2022): 130. http://dx.doi.org/10.3390/axioms11030130.

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This paper is centered around the creation of new fuzzy connectives using automorphism functions. The fuzzy connectives theory has been implemented in many problems and fields. In particular, the N-negations, t-norms, S-conorms and I-implications concepts played crucial roles in forming the theory and applications of the fuzzy sets. Thus far, there are multiple strategies for producing fuzzy connectives. The purpose of this paper is to provide a new strategy that is more flexible and fast in comparison with the rest. In order to create this method, automorphism and additive generator functions were utilized. The general formulas created with this method can provide new fuzzy connectives. The main conclusion is that new fuzzy connectives can be created faster and with more flexibility with our strategy.

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16

Agustin, Venny Riana Riana, and Wahyu Henky Irawan. "Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan." CAUCHY 4, no. 1 (November 15, 2015): 10. http://dx.doi.org/10.18860/ca.v4i1.3168.

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Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

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17

Stević, Željko, Dillip Kumar Das, Rade Tešić, Marijo Vidas, and Dragan Vojinović. "Objective Criticism and Negative Conclusions on Using the Fuzzy SWARA Method in Multi-Criteria Decision Making." Mathematics 10, no. 4 (February 18, 2022): 635. http://dx.doi.org/10.3390/math10040635.

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The quality of output or decision-making depends on high-quality input data, their adequate evaluation, the application of adequate approaches, and accurate calculation. In this paper, an objective criticism of applying the fuzzy SWARA (step-wise weight assessment ratio analysis) method based on the Chang TFN (triangular fuzzy number) scale is performed. Through research, it has been noticed that a large number of studies use this approach and, as an epilogue, there are wrong decisions based on inconsistent values in relation to the initial assessment of decision-makers (DMs). Seven representative studies (logistics, construction industry, financial performance management, and supply chain) with different parameter structures and decision matrix sizes have been singled out. The main hypothesis has been set, which implies that the application of this approach leads to wrong decisions because the weight values of the criteria are incorrect. A comparative analysis with the improved fuzzy SWARA (IMF SWARA) method has been created and a number of negative conclusions has been reached on using the fuzzy SWARA method and the Chang scale: Primarily, that using such an approach is impossible for two or more criteria to have equal value, that allocating TFN (1,1,1) leads to criteria values that are inconsistent with expert evaluation, that the last-ranked criteria in the fuzzy SWARA method have no influential value on the ranking of alternatives, that there is a great gap between the most significant and last-ranked criteria, and that the most significant criterion has a huge impact on the evaluation of alternative solutions and decision making. As a general conclusion, it is given that this approach is not adequate for application in problems of multi-criteria decision making because it produces inadequate management of processes and activities in various spheres.

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18

Abdy, Muhammad, Awi Dassa, and Sri Julia Nensi. "Konsep Himpunan Fuzzy pada Paradoks Russel." Journal of Mathematics, Computations, and Statistics 2, no. 2 (May 12, 2020): 189. http://dx.doi.org/10.35580/jmathcos.v2i2.12582.

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Himpunan fuzzy menggunakan dasar logika fuzzy untuk menyatakan suatu objek menjadi anggota dengan derajat keanggotaan ( ), tetapi Logika fuzzy melanggar hukum logika biner sehingga muncul anggapan bahwa logika fuzzy memiliki masalah yang sama dengan paradoks. Tetapi nilai kebenarana logika fuzzy tergantung dari derajat keanggotaan yang dimilikinya sehingga dapat ditarik sebuah kesimpulan dari besar darajat keanggotaan tersebut, sedangkan paradoks nilai kebenarannya tidak dapat ditarik kesimpulan apapun. Paradoks merupakan bentuk kritik landasan yang bertujuan untuk mengungkapkan dan menentukan inkonsistensi atau kontradiksi yang dihasilkan dari beberapa eksperimen mental dalam matematika, salah satu paradoks yang terkenal dalam kritik landasan teori himpunan adalah paradok Russel Pemecahan paradoks Russel dengan menggunakan konsep teori himpunan fuzzy diperoleh derajat keanggotaan adalah 0.5 merupakan pernyataan setengah benar (half true) dan adalah 0.5 jugan merupakan pernyataan setengah benar (half true). Kata kunci: Logika fuzzy, himpunan fuzzy, paradoks, paradoks Russel.Fuzzy sets use the basis of fuzzy logic to declare an object to be a member with the degree of membership ( ), but fuzzy logic violates the law of binary logic so that the assumption arises that fuzzy logic has the same problem with paradox. But the true value of fuzzy logic depends on the degree of membership it has so that a conclusion can be drawn from the large membership ranks, while the paradox of its value cannot be drawn any conclusions. The paradox is a form of ground criticism that aims to express and determine the inconsistencies or contradictions that result from several mental experiments in mathematics, one of the paradoxes that is well-known in critics of set theory is Russel's paradox . The paradoxical solution of Russell by using fuzzy set theory concepts is that the degree of membership is 0.5 and is 0.5.Keywords: Fuzzy Logic, fuzzy set, paradox, Russel paradox.

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19

OH, SUNG-KWUN, DONG-WON KIM, and WITOLD PEDRYCZ. "HYBRID FUZZY POLYNOMIAL NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, no. 03 (June 2002): 257–80. http://dx.doi.org/10.1142/s0218488502001478.

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We propose a hybrid architecture based on a combination of fuzzy systems and polynomial neural networks. The resulting Hybrid Fuzzy Polynomial Neural Networks (HFPNN) dwells on the ideas of fuzzy rule-based computing and polynomial neural networks. The structure of the network comprises of fuzzy polynomial neurons (FPNs) forming the nodes of the first (input) layer of the HFPNN and polynomial neurons (PNs) that are located in the consecutive layers of the network. In the FPN (that forms a fuzzy inference system), the generic rules assume the form "if A then y = P(x) " where A is fuzzy relation in the condition space while P(x) is a polynomial standing in the conclusion part of the rule. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as constant, linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are considered. Each PN of the network realizes a polynomial type of partial description (PD) of the mapping between input and out variables. HFPNN is a flexible neural architecture whose structure is based on the Group Method of Data Handling (GMDH) and developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. The experimental part of the study involves two representative numerical examples such as chaotic time series and Box-Jenkins gas furnace data.

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20

Єпік М. О. "МЕХАНІЗМ НЕЧІТКОГО ВИВЕДЕННЯ В ІНТЕЛЕКТУАЛЬНІЙ СИСТЕМІ ДІАГНОСТИКИ ЗАХВОРЮВАНЬ". Science Review, № 2(19) (28 лютого 2019): 3–9. http://dx.doi.org/10.31435/rsglobal_sr/28022019/6363.

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This article is devoted consideration of mechanism of fuzzy conclusion in the intellectual system of diagnostics of diseases. The process of realization of inference method is described. The general structure of model of diagnostics of diseases is presented. The example of model fragment is considered. Description of base of fuzzy rules of the intellectual system is presented. The examples of external and internal representation of rules are resulted. The stages of algorithm of fuzzy conclusion of Mamdani are considered. Description of application of algorithm is presented for Mamdani for the intellectual system of diagnostics of diseases.

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21

Seletkov, Ilya. "APPLICATION OF MATRIX APPROACH OF FUZZY LOGIC FOR DECISION SUPPORT IN OIL MINING EQUIPMENT SERVICE." Applied Mathematics and Control Sciences, no. 4 (December 15, 2020): 65–88. http://dx.doi.org/10.15593/2499-9873/2020.4.05.

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The work solves the problem of building an intelligent decision support system for servicing oil production equipment. At the first stage – the choice of an intelligent model – it is shown that in the existing conditions it is difficult to obtain a training sample in digital form. On the other hand, there is an opportunity to gain knowledge of subject matter experts – masters and technologists – in the form of a set of linguistic rules. Based on this, a conclusion about the effectiveness of the use of fuzzy logic to solve this problem is made. At the stage of constructing an intelligent model, the use of the matrix approach of fuzzy logic is proposed. To elaborate this approach an algorithm of fuzzy inference based on vector fuzzy predicates is developed. Capabilities and advantages of new algorithm are demonstrated. In particular, it is shown that the matrix representation makes possible reducing computations to solving a system of linear equations. Matrix inference also allows to explicitly determine the range of values of the analyzed parameters at which the knowledge base does not allow making a clear conclusion. A model of a fuzzy logic machine in the form of a fuzzy combinational circuit that analyzes an external memory block is proposed for the analysis of retrospective information on the change in the values of the parameters of technological equipment over time. Specific cases allowing the transition from a state machine to a combinational circuit are shown. Article also shows how this can be done. The main advantage of this approach is the absence of the need to use the difficult to formalize concept of a fuzzy state, which leads to a simplified construction of fuzzy logical devices with memory. At the end work contains brief conclusions about the application of the proposed methods and algorithms for building, testing, implementing a decision support system and about its effectiveness.

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22

Dong, Fang Liang. "A Improvement Analysis Model Based on Fuzzy Parameters Reliability." Advanced Materials Research 487 (March 2012): 764–69. http://dx.doi.org/10.4028/www.scientific.net/amr.487.764.

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Fuzzy Structure; Fuzzy Parameters; Reliability; Membership Function Abstract. This paper takes the fuzzy mathematics' reliability analysis as study object, has conducted the research to the fuzzy parameter system's reliability; comparing with traditional reliability analysis, obtained more precise fuzzy reliability confidence interval formula and carried on the proving based on cascade system, which has solved the computation complexity and the low precise problem in the tradition fuzzy reliability analysis's mass fuzzy operation. Method in the paper has certain promotional value in the fuzzy problem's research. It solved some problems such as the traditional fuzzy reliability analysis in the mass caused by the calculation of fuzzy computing complexity and low-precision. The emphasis of the article is divided into two parts: One is analysis and discussion on traditional module reliability analysis method and the fuzzy number membership function; the other one is proposing one new fuzzy parameter reliability analysis method and the conclusion, based on the former analysis.

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Li, Ping, Chang Feng Yan, Ling Ke Zeng, and Xiao Su Cheng. "Simulation and Application of Fuzzy Control System with Integrator." Advanced Materials Research 418-420 (December 2011): 1825–28. http://dx.doi.org/10.4028/www.scientific.net/amr.418-420.1825.

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This article simulated fuzzy control system by using MATLAB and compared the normal fuzzy control system with the fuzzy control system which has an integrator. Further more, the influence of quantize factors, proportional factors and integral constants on system was studied. The simulation results show that static error always exists in normal fuzzy control systems and proportional factors influence the stability of the system greatly. Put an integrator into a fuzzy control system, and static error can be eliminated and the system stability can be improved. The conclusion of simulation and practical experience, use fuzzy control system with integrator in reality. It can get good results.

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Wang, Zhen Ya, Xiao Yan Cong, and Yan Dong Wang. "Subjective Evaluation of Construction Machinery Cab’s Comfort Based on Fuzzy Set Theory." Advanced Materials Research 211-212 (February 2011): 600–604. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.600.

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This paper applied Fuzzy set theory to the comfort evaluation of construction machinery cab. Fuzzy comprehensive evaluation model, evaluation indexes set and comment set are built up. A relatively objective conclusion to an excavator cab’s comfort is obtained by calculation. This study puts forward a strict accurate handling ways to solve fuzzy phenomena in subjective evaluation of construction machinery cab’s comfort.

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25

Tsukamoto, Yahachiro. "A Normative Approach to Fuzzy Logic Reasoning Using Residual Implications." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (May 20, 2009): 262–67. http://dx.doi.org/10.20965/jaciii.2009.p0262.

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Анотація:

Logical problems with fuzzy implications have been investigated minutely (Baczynski and Jayaram [1]). Considering some of the normative criteria to be met bygeneralized modus ponens, we have formulated a method of fuzzy reasoning based on residual implication. Among these criteria, the specificity possessed by the conclusion deduced bygeneralized modus ponensshould not be stronger than that of the consequent in the fuzzy implication.

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26

Kwak, Sonil, Unha Kim, Kumju Kim, Ilmyong Son, and Chonghan Ri. "Fuzzy Reasoning Method Based on Distance Measure and Its Reductive Property." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 8 (June 1, 2020): 73–89. http://dx.doi.org/10.37394/232018.2020.8.10.

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Анотація:

This paper shows a basic and original fuzzy reasoning method that can draw a novel study direction of the approximate inference in fuzzy systems with uncertainty. Firstly we propose a criterion function for checking of the reductive property about fuzzy modus ponens (FMP) and fuzzy modus tollens (FMT). Secondly unlike fuzzy reasoning methods based on the similarity measure, we propose a principle of new fuzzy reasoning method based on distance measure and then present two theorems for FMP and FMT. Thirdly through the several computational experiments, we show that proposed method is simple and effective, and in accordance with human thinking. Finally we pointed out conclusion that proposed method does satisfy the convergence of the fuzzy control and has not information loss.

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27

Xiao, Lei, Li Li, and Xiao Long Wu. "Developing Fuzzy Control Irrigate System Based on PLC." Applied Mechanics and Materials 721 (December 2014): 261–64. http://dx.doi.org/10.4028/www.scientific.net/amm.721.261.

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Анотація:

This paper will describe that the fuzzy control is used to realize irrigate real-time control. And some reasonable fuzzy rules are found by computer simulation in MATLAB. Then the real-time irrigate will be applied by fuzzy control rules with Programmable Logic Controller (PLC) circuit. At last, writer made a conclusion that debugging greenhouse seedlings is well to meet the requirements of greenhouse.

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Santika, Gayatri Dwi, and Wayan F. Mahmudy. "The Effect of External Factors on Consumption Electricity Loads Forecasting using Fuzzy Takagi-Sugeno Kang." MATICS 9, no. 1 (March 21, 2017): 1. http://dx.doi.org/10.18860/mat.v9i1.3968.

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Анотація:

<strong>This study applied Fuzzy Inference System Sugeno to forecast electrical load by considering the external factors. To see the accuracy of forecasting using Fuzzy Inference System Sugeno, then a comparison between the forecasting results of Fuzzy Inference System Sugeno using historical data with Fuzzy Inference System Sugeno using external factors was done. By using external factors method, resulted the smallest RMSE of 0762 and using historical data obtained error (RMSE) of 1028. The results of the study came to the conclusion that Fuzzy Inference System Sugeno method using external factors to forecast the consumption of electrical load gives a better result than Fuzzy Inference System Sugeno using only historical data.</strong>

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Maity, Suman, Avishek Chakraborty, Sujit Kumar De, Sankar Prasad Mondal, and Shariful Alam. "A comprehensive study of a backlogging EOQ model with nonlinear heptagonal dense fuzzy environment." RAIRO - Operations Research 54, no. 1 (January 2020): 267–86. http://dx.doi.org/10.1051/ro/2018114.

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This paper deals with an adaptation of an application of nonlinear heptagonal dense fuzzy number. The concept of linear and as well as non-linear for both symmetric and asymmetric heptagonal dense fuzzy number is introduced here. We develop a new ranking method for non-linear heptagonal dense fuzzy number also. Considering a backorder inventory model with non-linear heptagonal dense fuzzy demand rate we have utilized a modified centroid method for defuzzification. For decision maker’s aspects, numerical examples, comparative study with other dense fuzzy numbers and a sensitivity analysis show the superiority of the nonlinear heptagonal dense fuzzy number. Finally, graphical illustrations are made to justify the model followed by a conclusion.

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Azad, Farheen. "A Review on the Development of Fuzzy Classifiers with Improved Interpretability and Accuracy Parameters." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 2, no. 2 (June 4, 2021): 1–9. http://dx.doi.org/10.54060/jieee/002.02.020.

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This review paper of fuzzy classifiers with improved interpretability and accuracy parameter discussed the most fundamental aspect of very effective and powerful tools in form of probabilistic reasoning, The fuzzy logic concept allows the effective realization of ap-proximate, vague, uncertain, dynamic, and more realistic conditions, which is closer to the actual physical world and human thinking. The fuzzy theory has the competency to catch the lack of preciseness of linguistic terms in a speech of natural language. The fuzzy theory provides a more significant competency to model humans like com-mon-sense reasoning and conclusion making to fuzzy set and rules as good membership function. Also, this paper reviews discussed the evaluation of the fuzzy set, type-1, type-2, and interval type-2 fuzzy system from traditional Boolean crisp set logic along with interpretability and accuracy issues in the fuzzy system.

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31

Nesimovic, Sanela, and Dzenan Gusic. "Fuzzy Functional and Multivalued Dependencies for Frank’s Class of Additive Generators." International Journal of Circuits, Systems and Signal Processing 15 (January 18, 2021): 8–22. http://dx.doi.org/10.46300/9106.2021.15.2.

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In this paper we consider all possible dependencies that can be built upon similarity-based fuzzy relations, that is, fuzzy functional and fuzzy multivalued dependencies. Motivated by the fact that the classical obtaining of new dependencies via inference rules may be tedious and uncertain, we replace it by the automated one, where the key role is played by the resolution principle techniques and the fuzzy formulas in place of fuzzy dependencies. We prove that some fuzzy multivalued dependency is actively correct with respect to given fuzzy relation instance if and only if the corresponding fuzzy formula is in line with the attached interpretation. Additionally, we require the tuples of the instance to be conformant (up to some extent) on the leading set of attributes. The equivalence as well as the conclusion are generalized to sets of attributes. The research is conducted by representing the attributes and fuzzy dependencies in the form of fuzzy formulas, and the application of fuzzy implication operators derived from carefully selected Frank’s classes of additive generators

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Teshnehlab, Mohammad, and Keigo Watanabe. "Robot Manipulator Control Using Fuzzy Gaussian Potential Neural Networks." Journal of Robotics and Mechatronics 7, no. 1 (February 20, 1995): 21–28. http://dx.doi.org/10.20965/jrm.1995.p0021.

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This paper describes the complete flexible design of a fuzzy gaussian potential neural network (FGPNN) having the ability to learn expert control rules of fuzzy controller. The proposed structure consists of gaussian potential function (GPF) which is utilized in the antecedent as the membership function, and the flexible bipolar sigmoid function (FBSF) is utilized in the conclusion part. The GPF enables a reduction in the number of labelings in the antecedent, and the FBSF leads to a reduction in the learning load in the conclusion and captures the linearity and/or nonlinearity of the system in the conclusion. The proposed construction reduces the complexity to a simple design in the antecedent, especially for large-scale inputs, thus shortening the time for learning with learning sigmoid function parameters (SFPs) in the conclusion part only. Finally, the simulations of two-link manipulator will be provided for both the conventional and proposed FGPNN controller in order to evaluate the newly designed controller.

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Solatikia, Farnaz, Erdem Kiliç, and Gerhard Wilhelm Weber. "Fuzzy optimization for portfolio selection based on Embedding Theorem in Fuzzy Normed Linear Spaces." Organizacija 47, no. 2 (May 1, 2014): 90–97. http://dx.doi.org/10.2478/orga-2014-0010.

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Abstract Background: This paper generalizes the results of Embedding problem of Fuzzy Number Space and its extension into a Fuzzy Banach Space C(Ω) × C(Ω), where C(Ω) is the set of all real-valued continuous functions on an open set Ω. Objectives: The main idea behind our approach consists of taking advantage of interplays between fuzzy normed spaces and normed spaces in a way to get an equivalent stochastic program. This helps avoiding pitfalls due to severe oversimplification of the reality. Method: The embedding theorem shows that the set of all fuzzy numbers can be embedded into a Fuzzy Banach space. Inspired by this embedding theorem, we propose a solution concept of fuzzy optimization problem which is obtained by applying the embedding function to the original fuzzy optimization problem. Results: The proposed method is used to extend the classical Mean-Variance portfolio selection model into Mean Variance-Skewness model in fuzzy environment under the criteria on short and long term returns, liquidity and dividends. Conclusion: A fuzzy optimization problem can be transformed into a multiobjective optimization problem which can be solved by using interactive fuzzy decision making procedure. Investor preferences determine the optimal multiobjective solution according to alternative scenarios.

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Sugiyarto, Sugiyarto, Joko Eliyanto, Nursyiva Irsalinda, Zhurwahayati Putri, and Meita Fitrianawat. "A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN." JTAM (Jurnal Teori dan Aplikasi Matematika) 5, no. 1 (April 17, 2021): 110. http://dx.doi.org/10.31764/jtam.v5i1.3766.

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Sentiment analysis is an analysis with an objective to identify like, dislike, comments, opinion, or feedback on certain content which will be categorized into positive, negative, or neutral. In general selection, sentiment analysis widely known to be used to predict the winner on election process. This method tries to dig the people sentiment on their governor candidates during election, whether it’s positive, negative, or neutral opinion. The output of the positive sentiment is related to people acceptance towards one of the election nominee. That statement usually applied as a base reference for determining the result of the election process. In sentiment analysis, the importance of its fuzzy logics must be considered. Each of the people statement is assumed to have the level of positive, negative, or neutral percentage. The concept of fuzzy logic is developed and applied on one of this text mining method. This research is focusing on comparison analysis and fuzzy logic application in sentiment analysis method. Two method which discussed in this research are Fuzzy Naïve Bayes and Sentiment Fuzzy with convolutional neural network. This research is applied on PILKADA of Solo and Medan district case study. The data of the people opinion are acquired from twitter and collected on September 2020 to December 2020. The two methods which mentioned before are implemented on the acquired data and the output of these method application then compared. The conclusion of this research suggest that different approach will resulting in different output.

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NGUYEN, CAT HO, DINH KHANG TRAN, HUYNH VAN NAM, and HAI CHAU NGUYEN. "HEDGE ALGEBRAS, LINGUISTIC-VALUE LOGIC AND THEIR APPLICATION TO FUZZY REASONING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 04 (August 1999): 347–61. http://dx.doi.org/10.1142/s0218488599000301.

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People use natural languages to think, to reason, to deduce conclusions, and to make decisions. Fuzzy set theory introduced by L. A. Zadeh has been intensively developed and founded a computational foundation for modeling human reasoning processs. The contribution of this theory both in the theoretical and the applied aspects is well recognized. However, the traditional fuzzy set theory cannot handle linguistic terms directly. In our approach, we have constructed algebraic structures to model linguistic domains, and developed a method of linguistic reasoning, which directly manipulates linguistic terms, In particular, our approach can be applied to fuzzy control problems. In many applications of expert systems or fuzzy control, there exist numerous fuzzy reasoning methods. Intuitively, the effectiveness of each method depends on how well this method satisfies the following criterion: the similarity degree between the conclusion (the output) of the method and the consequence of an if-then sentence (in the given fuzzy model) should be the "same" as that between the input of the method and the antecedent of this if-then sentence. To formalize this idea, we introduce a "measure function" to measure the similarity between linguistic terms in a domain of any linguistic variable and to build approximate reasoning methods. The resulting comparison between our method and some other methods shows that our method is simple and more effective.

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36

Reddy, Bapatu Siva Kumar, and P. Vishnu Vardhan. "Novel Alphabet Deduction Using MATLAB by Neural Networks and Comparison with the Fuzzy Classifier." Alinteri Journal of Agriculture Sciences 36, no. 1 (June 29, 2021): 623–28. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21088.

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Aim: The study aims to identify or recognize the alphabets using neural networks and fuzzy classifier/logic. Methods and materials: Neural network and fuzzy classifier are used for comparing the recognition of characters. For each classifier sample size is 20. Character recognition was developed using MATLAB R2018a, a software tool. The algorithm is again compared with the Fuzzy classifier to know the accuracy level. Results: Performance of both fuzzy classifier and neural networks are calculated by the accuracy value. The mean value of the fuzzy classifier is 82 and the neural network is 77. The recognition rate (accuracy) with the data features is found to be 98.06%. Fuzzy classifier shows higher significant value of P=0.002 < P=0.005 than the neural networks in recognition of characters. Conclusion: The independent tests for this study shows a higher accuracy level of alphabetical character recognition for Fuzzy classifier when compared with neural networks. Henceforth, the fuzzy classifier shows higher significant than the neural networks in recognition of characters.

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Qiyas, Muhammad, Muhammad Ali Khan, Saifullah Khan, and Saleem Abdullah. "Concept of Yager operators with the picture fuzzy set environment and its application to emergency program selection." International Journal of Intelligent Computing and Cybernetics 13, no. 4 (October 12, 2020): 455–83. http://dx.doi.org/10.1108/ijicc-06-2020-0064.

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PurposeThe aim of this study as to find out an approach for emergency program selection.Design/methodology/approachThe authors have generated six aggregation operators (AOs), namely picture fuzzy Yager weighted average (PFYWA), picture fuzzy Yager ordered weighted average, picture fuzzy Yager hybrid weighted average, picture fuzzy Yager weighted geometric (PFYWG), picture fuzzy Yager ordered weighted geometric and picture fuzzy Yager hybrid weighted geometric aggregations operators.FindingsFirst of all, the authors defined the score and accuracy function for picture fuzzy set (FS), and some fundamental operational laws for picture FS using the Yager aggregation operation. After that, using the developed operational laws, developed some AOs, namely PFYWA, picture fuzzy Yager ordered weighted average, picture fuzzy Yager hybrid weighted average, PFYWG, picture fuzzy Yager ordered weighted geometric and picture fuzzy Yager hybrid weighted geometric aggregations operators, have been proposed along with their desirable properties. A decision-making (DM) approach based on these operators has also been presented. An illustrative example has been given for demonstrating the approach. Finally, discussed the comparison of the proposed method with the other existing methods and write the conclusion of the article.Originality/valueTo find the best alternative for emergency program selection.

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Jing, Guo Lin, Wen Ting Du, Xiang Chen, and Huan Yi. "Prediction Model in Electrodialysis Process Based on ANFIS." Advanced Materials Research 268-270 (July 2011): 332–35. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.332.

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Fuzzy system is known to predict model in the electrodialysis process. This paper aimed to predict separation percent(SP) of NaCl solution as a function of concentration, temperature, flow rate and voltage. Besides, in the MATLAB, ANFIS(Adaptive Neuro-Fuzzy Inference System) based on Sugeno fuzzy model, its structure was similar to neural network and could generate fuzzy rules automatically. We obtained fitted values of SP by ANFIS. Then, we studied these influencing factors on fitted values of SP. Finally, we draw a conclusion that SP is in direct proportion to temperature and voltage, but in inverse proportion to concentration and flow rate.

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39

Yiarayong, Pairote. "On 2-absorbing bipolar fuzzy ideals over LA -semigroups." Journal of Intelligent & Fuzzy Systems 41, no. 2 (September 15, 2021): 3173–81. http://dx.doi.org/10.3233/jifs-210388.

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The aim of this manuscript is to apply bipolar fuzzy sets for dealing with several kinds of theories in LA -semigroups. To begin with, we introduce the concept of 2-absorbing (quasi-2-absorbing) bipolar fuzzy ideals and strongly 2-absorbing (quasi-strongly 2-absorbing) bipolar fuzzy ideals in LA -semigroups, and investigate several related properties. In particular, we show that a bipolar fuzzy set A = ( μ A P , μ A N ) over an LA -semigroup S is weakly 2-absorbing if and only if [ B ⊙ C ] ⊙ D ⪯ A implies B ⊙ C ⪯ A or C ⊙ D ⪯ A or B ⊙ D ⪯ A for any bipolar fuzzy sets B = ( μ B P , μ B N ) , C = ( μ C P , μ C N ) and D = ( μ D P , μ D N ) . Also, we give some characterizations of quasi-strongly 2-absorbing bipolar fuzzy ideals over an LA -semigroup S by bipolar fuzzy points. In conclusion of this paper we prove that the relationship between quasi-strongly 2-absorbing bipolar fuzzy ideals over an LA -semigroup S and quasi-2-absorbing bipolar fuzzy ideals over S.

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Acharya, Falguni, Vandana Kushawaha, Jitendra Panchal, and Dimplekumar Chalishajar. "Controllability of Fuzzy Solutions for Neutral Impulsive Functional Differential Equations with Nonlocal Conditions." Axioms 10, no. 2 (May 6, 2021): 84. http://dx.doi.org/10.3390/axioms10020084.

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In this paper, the controllability of fuzzy solutions for first order nonlocal impulsive neutral functional differential equations is explored using the Banach fixed point theorem. We utilized the concepts of the fuzzy set theory, functional analysis, and the Hausdorff metric. In the conclusion, an illustration is given to bolster the hypothesis.

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41

Fazliana Fadzail, Noor, Samila Mat Zali, Arizadayana Zahalan, and Mohd Alif Ismail. "Parameter Estimation for Dynamic Model of Distribution Network Cell (DNC) Using Fuzzy System." Applied Mechanics and Materials 793 (September 2015): 489–93. http://dx.doi.org/10.4028/www.scientific.net/amm.793.489.

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The aim of this project is to develop parameter estimation for dynamic model of distribution network cell (DNC) using fuzzy system. The parameter value was updated through adaptive neuro-fuzzy inference system (ANFIS). The active and reactive power responses from the fuzzy model were compared with the response from the full DNC model at various types of disturbances. The response of full DNC model was obtained from the UK 11 kV distribution network model. The model was built in DigSILENT PowerFactory software. The results obtained shown that the fuzzy model was more simple as only a few parameters involved in developing the equivalent model. This simplicity was reflected in the low computational time. In conclusion, the parameter estimation using fuzzy system was successfully developed.

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42

Harmati, István Á., Ádám Bukovics, and László T. Kóczy. "Minkowski’s Inequality Based Sensitivity Analysis of Fuzzy Signatures." Journal of Artificial Intelligence and Soft Computing Research 6, no. 4 (October 1, 2016): 219–29. http://dx.doi.org/10.1515/jaiscr-2016-0016.

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Abstract Fuzzy signatures were introduced as special tools to describe and handle complex systems without their detailed mathematical models. The input parameters of these systems naturally have uncertainties, due to human activities or lack of precise data. These uncertainties influence the final conclusion or decision about the system. In this paper we discuss the sensitivity of the weigthed general mean aggregation operator to the uncertainty of the input values, then we analyse the sensitivity of fuzzy signatures equipped with these aggregation operators. Finally, we apply our results to a fuzzy signature used in civil enginnering.

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Maity, Somnath, and Sankar Kumar Roy. "A New Approach for Solving Type-2-Fuzzy Transportation Problem." International Journal of Mathematical, Engineering and Management Sciences 4, no. 3 (June 1, 2019): 683–96. http://dx.doi.org/10.33889//ijmems.2019.4.3-054.

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In this paper, a new approach is introduced to solve transportation problem with type-2-fuzzy variables. In most of the real-life situations, the available data do not happen to be crisp in nature. It gives rise to the fuzzy transportation problem (FTP). This proposed approach concentrates on the problem when the vertical slices of type-2-fuzzy sets (T2FSs) are trapezoidal fuzzy numbers (TFNs). The original problem reduces to three different linear programming problems (LPPs) which are solved using the simplex algorithm. Then the effectiveness of this paper is discussed with numerical example. In conclusion, the significance of the paper and the scope of future study are discussed.

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Jin, Shangzhu, Jun Peng, and Dong Xie. "A New MapReduce Approach with Dynamic Fuzzy Inference for Big Data Classification Problems." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 3 (July 2018): 40–54. http://dx.doi.org/10.4018/ijcini.2018070103.

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Currently, big data and its applications have become one of the emergent topics. In practice, MapReduce framework and its different extensions are the most popular approaches for big data. Fuzzy system based models stand out for many applications. However, when a given observation has no overlap with antecedent values, no rule can be invoked in classical fuzzy inference can also appear in big data environment, and therefore no consequence can be derived. Fortunately, fuzzy rule interpolation techniques can support inference in such cases. Combining traditional fuzzy reasoning technique and fuzzy interpolation method may promote the accuracy of inference conclusion. Therefore, in this article, an initial investigation into the framework of MapReduce with dynamic fuzzy inference/interpolation for big data applications (BigData-DFRI) is reported. The results of an experimental investigation of this method are represented, demonstrating the potential and efficacy of the proposed approach.

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Yang, Xiaojun, Gang Liu, Jinku Guo, Hongqiao Wang, and Bing He. "The Robust Passive Location Algorithm for Maneuvering Target Tracking." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/404986.

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With the advantages such as high security and far responding distance, the passive location has a broad application in military and civil domains such as radar and aerospace. However, most of the current passive location methods are based on the framework of the probability theory and cannot be used to deal with fuzzy uncertainty in the passive location systems. Though the fuzzy Kalman filter can be used in the uncertainty systems, it could not deal with the abrupt change of state like the maneuvering target which will lead to the filter divergence. Therefore, in order to track the maneuvering target in the fuzzy passive system, we proposed a robust fuzzy extended Kalman filter based on the orthogonality principle and the fuzzy filter in the paper. Conclusion can be made based on the simulation result that this new approach is more precise and more robust than the fuzzy filter.

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Czebe, András. "Fuzzy logic behind forensic identity." Belügyi Szemle 68, no. 2 (September 15, 2020): 11–22. http://dx.doi.org/10.38146/bsz.spec.2020.2.1.

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With the development of forensic sciences during the 20th century, macro-scaled empirical relations were supplemented with micro- and submicro-scaled probability relations. High sensitivity analysis methods imposed increasingly stringent criteria on the science of individualization. This process even labelled those traditional forensic sciences junks, which rely heavily on an indefinable set of characteristics in order to achieve individuality. However, this has not led to a systematic change in the judicial interpretation of expert evidence. In this paper I will therefore address the theoretical question: What logic lies behind forensic identity? In order to answer this question, I conducted explanatory research in the fields of forensics, criminal law, philosophy and logic. Following the collection and interpretation of qualitative data, such as the relevant literature, legislation and case law, I came to the conclusion that fuzzy logic lies behind forensic identity.

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Carvalho, Lucimar M. F. de, Silvia Modesto Nassar, Fernando Mendes de Azevedo, Hugo José Teixeira de Carvalho, Lucas Lese Monteiro, and Ciciliana M. Zílio Rech. "A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations." Arquivos de Neuro-Psiquiatria 66, no. 2a (June 2008): 179–83. http://dx.doi.org/10.1590/s0004-282x2008000200007.

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OBJECTIVE: To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. METHOD: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication) architecture and an artificial neural network with backpropagation learning algorithm (ANNB). RESULTS: The study was composed by 244 patients with a bigger frequency of the feminine sex. The number of right decisions at the test phase, obtained by the NEFCLASS and ANNB was 83.60% and 90.16%, respectively. The best sensibility result was attained by NEFCLASS (84.90%); the best specificity result were attained by ANNB with 95.65%. CONCLUSION: The proposed neuro-fuzzy system combined the artificial neural network capabilities in the pattern classifications together with the fuzzy logic qualitative approach, leading to a bigger rate of system success.

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Gao, Feng, Lin Jing Xiao, Wei Yan Zhong, and Wei Liu. "Fault Diagnosis of Shearer Based on Fuzzy Inference." Applied Mechanics and Materials 52-54 (March 2011): 1577–80. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.1577.

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The purpose of this study is to provide a correct and timely diagnosis mechanism of shearer failures by knowledge acquisition through a fuzzy inference system which could approximate expert experience. Concerning a question of uncertain knowledge expression and reasoning in shearer malfunction, the fuzzy inference theory is used in shearer malfunction fault diagnosis. The fuzzy relation matrix of faults and signs is deduced based on deep research of failure mechanism and expert experience, which agrees with fault and fault symptoms non one-to-one relationship and human thinking. Fault characteristic parameter is calculated to corresponding subordinate degree, then is operated with fuzzy relation matrix and get fault fuzzy vector. Finally, the shearer malfunction fault is diagnosed according to certain diagnosis principle. The example proves that the method has less calculation, explicit conclusion and other merits.

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P., Ananthi, and Parthipan V. "An Innovative Method for Detection of Malicious Behaviours in Automated Vehicle System Using Hybrid Fuzzy C-Means Algorithm with Neural Network Algorithm Based Accuracy and Cost." ECS Transactions 107, no. 1 (April 24, 2022): 11765–79. http://dx.doi.org/10.1149/10701.11765ecst.

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Aim: To improve the predictive accuracy and cost analysis for malicious behaviors in automated vehicle systems using the Hybrid Fuzzy C-Means algorithm (HFCM) and Neural Network algorithm (NN). Materials and Methods: Accuracy is performed with two groups Fuzzy C-Means Algorithm and the Neural Network algorithm of sample size per group (N = 125). G power 80% threshold 0.05%, CI 95%. Mean and Standard deviation. Result: Independent sample T-Test was carried out using Fuzzy C-Means and Neural Network. C-means (92.1%) perform better than NN (89.6%). There is a statistically significant difference between Fuzzy C-means and with (p<0.01). Conclusion: The results conclude that the proposed Fuzzy C-Means algorithm helps to identify and detect with better accuracy and cost percentage at event systems.

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Ivasic-Kos, M., S. Ribaric, and I. Ipsic. "Multi-level Image Classification Using Fuzzy Petri Net." International Journal of Fuzzy Systems and Advanced Applications 9 (March 13, 2022): 50–56. http://dx.doi.org/10.46300/91017.2022.9.8.

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For a multi-level image classification, a knowledge representation scheme based on Fuzzy Petri Net with fuzzy inference algorithms is used. A simple graphical Petri net notation and a welldefined semantics displaying the process of reasoning through inference trees are used for visualization of the knowledge base and explanations of derived conclusion. Used knowledge representation formalism has the ability to show a probability of concepts and relations. The procedures of image multi-level classification using fuzzy recognition and inheritance algorithms on a knowledge representation scheme, as well as experimental results of image semantic interpretation, are presented.

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