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Journal articles on the topic 'Fuzzy logic inference system'
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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 fact that the software implementation of the CANFIS model, based on the STL of the C++ language, is of great importance in the field of machine learning, artificial intelligence and data analysis. The work’s results can be applied in various fields, including when making decisions based on fuzzy logic. Special feature of the studied and developed model is to create an adaptive model capable of modeling systems with uncertainty and blurriness. The developed model is able to process data and make decisions based on fuzzy rules. CANFIS finds applications in various fields, including forecasting, management, classification and data analysis. It can be concluded that the developed neural network with fuzzy logic can be effectively applied in various fields where time series forecasting, system management and decision-making based on fuzzy information are used.
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Chandrasekhar, Tadi, and Ch Sumanth Kumar. "Improved Facial Identification Using Adaptive Neuro-Fuzzy Logic Inference System." Indian Journal Of Science And Technology 16, no. 13 (2023): 1014–20. http://dx.doi.org/10.17485/ijst/v16i13.1833.
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Chang, Te-Chuan, C. William Ibbs, and Keith C. Crandall. "A fuzzy logic system for expert systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 2, no. 3 (1988): 183–93. http://dx.doi.org/10.1017/s0890060400000640.
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Using the theory of fuzzy sets, this paper develops a fuzzy logic reasoning system as an augmentation to a rule-based expert system to deal with fuzzy information. First, fuzzy set theorems and fuzzy logic principles are briefly reviewed and organized to form a basis for the proposed fuzzy logic system. These theorems and principles are then extended for reasoning based on knowledge base with fuzzy production rules. When an expert system is augmented with the fuzzy logic system, the inference capability of the expert system is greatly expanded; and the establishment of a rule-based knowledge base becomes much easier and more economical. Interpretations of the system’s power and possible future research directions conclude the paper.
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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 set of admissible ones, with specific values of conditions and conclusions for describing three types of fuzzy models of the processes and tasks under study. Using modules of standard algorithms and programs, algorithms and a program for solving problems of fuzzy inference systems and making semi-structured decisions based on the constructed fuzzy logic model were developed. This problem is solved by formalization methods based on the theory of algorithmization, fuzzy sets, and fuzzy inference.
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Ganzhur, Marina, Alexey Ganzhur, Nikita Dyachenko, Andrey Kobylko, and Alexander Melnikov. "Data analysis using system modeling." E3S Web of Conferences 389 (2023): 07005. http://dx.doi.org/10.1051/e3sconf/202338907005.
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Expert systems are increasingly being used to format safe operations. But the functions of expert systems can perform not only assistance in making decisions, but also analyze processes and help at various stages. These actions are possible when considering a system with fuzzy data. The work is devoted to solving the problem of fuzzy inference knowledge in intelligent systems based on the use of fuzzy logic. The scheme of construction of continuous logic, the computation of values of membership functions of linguistic variables of output knowledge. The proposed approach is based on the use of continuous logic, which allows for a more accurate representation of fuzzy data compared to traditional logic. The construction of the continuous logic scheme involves the use of fuzzy sets for both input and output variables, which are then used to compute the values of membership functions of linguistic variables of output knowledge. In this approach, the expert system is able to analyze processes and assist at various stages by making use of fuzzy inference knowledge. The fuzzy inference mechanism is based on the use of fuzzy logic, which allows for a more nuanced understanding of complex systems and processes. The expert system is able to analyze data from various sources and make informed recommendations based on the available information. Overall, the use of expert systems based on fuzzy logic is becoming increasingly popular in a variety of industries. By improving the accuracy of data analysis and decision-making, these systems can help to ensure safe and efficient operations.
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Bortolan, G. "An inference system based on fuzzy logic." Journal of Medical Engineering & Technology 22, no. 3 (1998): 112–20. http://dx.doi.org/10.3109/03091909809062476.
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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 paper also presents a running example aimed at showing the potential applicability of the proposal.
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PRANITA N. BALVE and JAYANTILAL N. PATEL. "Prediction of evapotranspiration using Fuzzy logic." Journal of Agrometeorology 18, no. 2 (2016): 311–14. http://dx.doi.org/10.54386/jam.v18i2.958.
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In this paper, evapotranspiration prediction is done using Fuzzy Inference System (FIS) of Fuzzy Logic.For the prediction of evapotranspiration, mean temperature, relative humidity, wind speed and net radiation is taken as inputs to the fuzzy inference system. To check the efficiency of the FIS model, the results were compared with the FAO-56 Penman Monteith (FPM-56) method. FIS model has given the coefficient of determination (R2) 0.979. Results indicated that, FIS model has better efficiency for prediction of evapotranspiration.
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Dr. R. Balasubrimanian, V. Belmer Gladson,. "A Novel Fuzzy Inference System Based Robust Reversible Watermarking Technique provided with Six Layer Security." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 661–78. http://dx.doi.org/10.17762/itii.v9i2.398.
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Digital Watermarking has evolved as one of the latest technologies for digital media copyright protection. Watermarking of images can be done in many ways and one of the proposed algorithms for image watermarking is by utilizing Fuzzy Logic. It is similar to the concept of a Fuzzy set, each element can be defined by an ordered pair, in which one is the value and other is the membership function value. Fuzzy logic systems can explain inaccurate information and explain their decisions. Fuzzy inference system is the simplest way of performing Fuzzy Logic. In the proposed method, three Fuzzy inference models are used to generate the weighing factor for embedding the watermark and input to the Fuzzy Inference System is taken from the Human Visual System model. The Performance measures used in the Process are Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Normalized Cross Correlation (NCC) and Bit Error Ratio (BER). The Proposed algorithm is immune to various Image Processing attacks.
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Болгов, А. А. "RISK ASSESSMENT USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM." ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ, no. 4(-) (December 23, 2022): 521–30. http://dx.doi.org/10.36622/vstu.2022.25.4.006.
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В работе предлагается использование адаптивной нейро-нечеткой системы вывода для оценки риска. Проводится подробный обзор адаптивной нейро-нечеткой системы вывода, выделяя основные свойства этой системы в области методов оценки рисков. Приведены основные преимущества использования адаптивной нейро-нечеткой системы вывода. Рассматривается архитектура адаптивной нейро-нечеткой системы вывода. Выделены и рассмотрены основные методы обучения системы. Предложены методы оценки эффективности модели на основе адаптивной нейро-нечеткой системы вывода для оценки риска. Представлен алгоритм внедрения адаптивной нейро-нечеткой системы вывода. Проводятся эксперименты, которые показывают влияние процесса обучения на форму функций принадлежности системы нечеткой логики. Выполнено сравнение результатов оценки риска, полученных с помощью нечеткой логики и при использовании адаптивной нейро-нечеткой системы выводы. The work proposes the use of an adaptive neuro-fuzzy inference system for risk assessment. A detailed review of the adaptive neuro-fuzzy inference system is carried out, highlighting the main properties of this system in the field of risk assessment methods. The main advantages of using an adaptive neuro-fuzzy inference system are given. The architecture of an adaptive neuro-fuzzy inference system is considered. The main methods of teaching the system are highlighted and considered. Methods for evaluating the effectiveness of the model based on an adaptive neuro-fuzzy inference system for risk assessment are proposed. An algorithm for implementing an adaptive neuro-fuzzy inference system is presented. Experiments are being conducted that show the influence of the learning process on the form of the membership functions of the fuzzy logic system. The results of risk assessment obtained using fuzzy logic and using adaptive neuro-fuzzy inference system are compared.
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Patel, Usha, Parita Rajiv Oza, Riya Revdiwala, Utsav Mukeshchandra Haveliwala, Smita Agrawal, and Preeti Kathiria. "Fuzzy Logic Inference-Based Automated Water Irrigation System." International Journal of Ambient Computing and Intelligence 13, no. 1 (2022): 1–15. http://dx.doi.org/10.4018/ijaci.304726.
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To fulfill the food interest of consistently expanding populace of our planet, it is important to do essential in the field of agribusiness. Traditional techniques for water systems like trench, wells, and precipitation are tedious and occasional. With the help of an automated water irrigation system the water, energy, and time can be moderated. This paper presents fuzzy rule logic inference-based automated water system framework. The soil moisture, weather forecast, crop status, and water-tank level are taken as input parameters. Soil moisture and water tank level can be recorded by utilizing sensors. The fuzzy logic-based system uses eighty-one rules to identify the amount of time to irrigate the fields. The emphasis is to solve agricultural problems by employing symbolic logic and to develop a system using computer science and mathematical logic. The use of such an automated system will decline costs, water prerequisite, and give power streamlining, with expanded proficiency.
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Sadaqat, Shama, Safiullah Junejo, and Saba Anwar. "Cloud Inspired Human Resource Management Empowered with Fuzzy Inference System." Research Journal for Societal Issues 5, no. 2 (2023): 409–27. http://dx.doi.org/10.56976/rjsi.v5i3.65.
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Traditional Human Resource Management (HRM) systems face complications in managing various issues related to personnel, organization, salary, attendance, etc. Cloud Computing (CC) provides help in these business applications. This research proposes a Cloud-Inspired Human Resource Management Information System (CIHRMIS) employing a fuzzy logic system. The proposed CIHRMIS, in view of Fuzzy Logic, may help companies accomplish their HRM tasks proficiently, which in turn reduces the cost and increases managerial efficiency. Further, the Fuzzy Logic System performs adaptive activities, which can be used with multiterminal platforms operation, which supplements the application value of the system as well.
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Tejash U. Chaudhari, Vimal B. Patel, Rahul G. Thakkar, and Chetanpal Singh. "Comparative analysis of Mamdani, Larsen and Tsukamoto methods of fuzzy inference system for students’ academic performance evaluation." International Journal of Science and Research Archive 9, no. 1 (2023): 517–23. http://dx.doi.org/10.30574/ijsra.2023.9.1.0443.
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Over the last few years, the use of the fuzzy logic technique for evaluating performance in the teaching-learning process is growing rapidly. In this research work, three different fuzzy inference methods: Mamdani fuzzy inference method, Larsen fuzzy inference method and Tsukamoto fuzzy inference method have been proposed for students' academic performance appraisal for multi-input variables. To obtain a degree of satisfaction, the Triangular membership function is used. The results of experiments showed the best fuzzy inference method among Mamdani, Larsen and Tsukamoto. We have also compared the results with the existing statistical method.
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Szili, Ferenc Ádám, János Botzheim, and Balázs Nagy. "Bacterial Evolutionary Algorithm-Trained Interpolative Fuzzy System for Mobile Robot Navigation." Electronics 11, no. 11 (2022): 1734. http://dx.doi.org/10.3390/electronics11111734.
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This paper describes the process of building a transport logic that enables a mobile robot to travel fast enough to reach a desired destination in time, but safe enough to prevent damage. This transport logic is based on fuzzy logic inference using fuzzy rule interpolation, which allows for accurate inferences even when using a smaller rule base. The construction of the fuzzy rule base can be conducted experimentally, but there are also solutions for automatic construction. One of them is the bacterial evolutionary algorithm, which is used in this application. This algorithm is based on the theory of bacterial evolution and is very well-suited to solving optimization problems. Successful transport is also facilitated by proper path planning, and for this purpose, the so-called neuro-activity-based path planning has been used. This path-planning algorithm is combined with interpolative fuzzy logic-based speed control of the mobile robot. By applying the described methods, an intelligent transport logic can be constructed. These methods are tested in a simulated environment and several results are investigated.
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Kopyrin, Andrey Sergeevich, and Alina Olegovna Kopyrina. "Development of the generic system of inference rules by knowledgebase." Программные системы и вычислительные методы, no. 1 (January 2021): 1–9. http://dx.doi.org/10.7256/2454-0714.2021.1.34798.
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The authors propose to align logical inference with the apparatus of fuzzy sets. When each solution is associated with a set of possible results with the known transitional probabilities, the solution is based on the digital information under uncertainty. Therefore, the main purpose of using fuzzy logic in expert systems consists in creation of computing devices (or software applications) that can imitate human-level reasoning and explain the techniques of decision-making. The goal of this research consists in detailed description of the reproducible standard method of setting rules of inference of the expert system for various economic subject fields, using a universal pattern of knowledgebase. For decision-making in a fuzzy system, the author suggests using the process of identification rule framework – determination of structural characteristics of fuzzy system, such as the number of fuzzy rules, number of linguistic terms the incoming variables are divided to. Such identification is conducted based on the fuzzy cluster analysis, using fuzzy decision trees. The authors present the structural chart of inference method on the basis of fuzzy logic. The presented in the article method of setting rules and fuzzy inference algorithm presented can be implemented in different areas of economics. The novelty of this work consists in automation and integration of the system for determination of fuzzy inference rules with the stage of input data collection in the subject field.
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Chang, Chung-Liang, and Ming-Fong Sie. "A Multistaged Fuzzy Logic Scheme in a Biobotanic Growth Regulation System." HortScience 47, no. 6 (2012): 762–70. http://dx.doi.org/10.21273/hortsci.47.6.762.
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A multistaged fuzzy logic control method was used in the development of a bionic botanical growth control system. The growth mode combined fuzzy logic inference with expert knowledge to regulate the growth rate of plants. First, environment factors such as the light, temperature, and water required for plants in different stages of growth were analyzed. Fuzzy logic was then used to establish membership functions, an inference engine, and rule table. An expert database related to plant growth was combined with the fuzzy logic controller to formulate a plant growth control system. Sunflowers were used as a simulated model and the results correspond to the information provided by experts. The proposed model was used to control the growth rate of plants based on data provided in the expert database. The proposed method and results of this study are applicable in the management and control of environments for the growth of crops.
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Graeme, Heald. "Issues with Reliability of Fuzzy Logic." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 829–34. https://doi.org/10.31142/ijtsrd18573.
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Since 1973, fuzzy logic has been rejected by a majority of linguists as a theory for dealing with vagueness in natural language. However, control engineers apply fuzzy vagueness in natural language to control system problems. A real world example of automatic fan control has shown that i fuzzy logic can be applied ii probability theory can better model the control system iii Boolean algebra with decision processes can also be applied. In addition, a redundancy issue for fuzzy logic has been raised. Moreover, a number of inherent flaws of fuzzy logic from an engineering viewpoint have been identified i fuzzy logic as probability ii fuzzy conjunction iii fuzzy disjunction iv fuzzy inference engine v fuzzy membership function. Fuzzy logic has been found to be an approximate model of probability, however, when viewed as probability fuzzy logical operations of OR and AND provide incorrect responses. Fuzzy inference will also fail when fuzzy logical operations are applied. Some theorists have attempted to fix the flaws of fuzzy logic in a system termed "Compensatory Fuzzy Logic , but the rules of CFL are ad hoc. As a result of the reliability and robustness concerns in fuzzy systems, safety may be compromised. Graeme Heald "Issues with Reliability of Fuzzy Logic" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: https://www.ijtsrd.com/papers/ijtsrd18573.pdf
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ANDRECUT, M., and M. K. ALI. "FUZZY REINFORCEMENT LEARNING." International Journal of Modern Physics C 13, no. 05 (2002): 659–74. http://dx.doi.org/10.1142/s0129183102003450.
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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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Imelda Nasrul, Tiara, Amri Amri, and Muhammad Sayuti. "Application of Fuzzy Mamdani Method to Predict the Number of Blood Bags Based on Demand and Supply Data Using Matlab." International Journal of Engineering, Science and Information Technology 4, no. 4 (2024): 29–37. http://dx.doi.org/10.52088/ijesty.v4i4.567.
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Fuzzy logic is a control system technique in solving problems and is applied to systems, from basic systems to difficult or complex systems. Fuzzy logic is the proper method to plan an input space into an output space using MATLAB's mathematical theory of fuzzy sets. The reason for using fuzzy logic is because it is related to uncertainty. The unstable demand for blood bags in hospitals makes the supply of blood bags excessive or lacking from demand. The lack of blood supply results in the unfulfilled demand for blood needed by the hospital, while the excess blood supply worsens the quality of blood. In this study, we will predict the number of bags produced using the Mamdani Fuzzy Inference System (FIS) method based on the minimum demand and maximum demand values and the minimum supply and maximum supply that produce output from the defuzzification process. Applying the Mamdani Fuzzy Inference System (FIS) method based on demand and supply data obtains optimal output with MATLAB in predicting the number of blood bags produced. The results of the study showed that the Mean Absolute Percentage Error (MAPE) fuzzy logic Mamdani error value was 24%, the accuracy value of the Fuzzy Inference System (FIS) Mamdani in determining the number of blood bag production was 76%, and the production output generated through the Fuzzy Inference System (FIS) Mamdani was 4,774 blood bags. The number of blood requests at the hospital is 4,443 blood bags, so the amount of blood that must be produced to meet the hospital's demand is 4,774 bags.
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Rosena Shintabella, Imma Ismaniar, Muhammad Fikry Januar, and Zulfikar Fauzi. "Loyal Customer Classification Using Fuzzy Logic Inference System." Journal of Applied Digital Business and Management 1, no. 2 (2024): 28–32. https://doi.org/10.71266/vrxmrn55.
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A company will be better if it can maintain its existence in the community, then the company is more oriented to acquire new customers.But one day it will lose customers if it is not managed properly. In this case there is a customer satisfaction variable that affects customer loyalty in a company. In this study apply fuzzy logic inference method to select customers with the highest level of loyalty. The extraordinary value of a customer towards a company certainly cannot be declared with certainty or exactness. So that the decision making is very suitable when using the concept of fuzzy logic, because it can represent variables that are vague or not exact. The case study in this study was conducted in a trading company or supermarket. The result of its implementation is that the system can determine who is the most loyal of several selected samples of customers.
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Ewaid, Salam Hussein, Turki Diwan Hussein, and Faiza Kadhim Emran. "Fuzzy Logic Inference Index to Assess the Water Quality of Tigris River within Baghdad City." Al-Mustansiriyah Journal of Science 29, no. 3 (2019): 16. http://dx.doi.org/10.23851/mjs.v29i3.617.
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This study aimed to develop a new water quality index for routine assessment of the river water quality for drinking purpose based on fuzzy logic artificial intelligence method. Four water quality parameters were involved in light of their significance to Iraqi waters, these parameters are biological oxygen demand, and total dissolved solids, total hardness, and fecal coliform. Fuzzy logic inference system with specific rules was developed by Matlab software using Mamdani fuzzy logic Max–Min inference system method. To evaluate the performance of this new fuzzy water quality index (FWQI), tests were conducted using the Iraqi standards for drinking water quality and the 2017 data set of Tigris River within Baghdad. Results revealed the FWQI ability to assess the water quality of Tigris River during the period of the study and that the method of fuzzy inference system was a simple, valuable and applied water quality evaluation tool for human drinking water of Iraqi rivers.
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Paika, Er Vishal, and Er Pankaj Bhambri. "Edge Detection Fuzzy Inference System." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 4, no. 1 (2013): 148–55. http://dx.doi.org/10.24297/ijmit.v4i1.811.
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In this paper a method has been developed for automatic edge detection of an digital image. An edge is made up of those pixels at which there is an abrupt change in the intensity. These pixels are known as edge pixels and are connected to give an edge. In this paper we have developed a mamdanis fuzzy inference system in MATLAB 2008 using fuzzy logic tool box. A smallest possible 2X2 window is used as a scanning mask. Mask slides over the whole image pixel by pixel, first horizontally in topmost horizontal line then after reaching at the end of line, it increments to check the next vertical location and it continues till the whole image is scanned. The FIS built has 4 inputs, each input representing a pixel for 2X2 mask, and 1 output that represents pixel under consideration. The rule editor consists of sixteen fuzzy rules. The results thus obtained are compared with Sobel edge operator and Canny edge operator.
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Lee-Kwang, Hyung, and Ju-Jang Lee. "Fuzzy Logic and Intelligence System." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 5 (2000): 319–20. http://dx.doi.org/10.20965/jaciii.2000.p0319.
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These papers are originally published in the proceedings of Korea fuzzy logic and intelligent systems society (KFIS) fall conference in 1999. Eight papers are selected for this special issue. Major topics of them are fuzzy theory, neural network, inference system, intelligent controller, etc. In this issue, Seihwan Park and Hyung Lee-Kwang extend the concept of fuzzy hypergraph to type-2 fuzzy hypergraph using type-2 fuzzy sets. It has not only the same properties of hypergraphs but also the extended properties of them. It is also shown that interval valued fuzzy hypergraph is a special case of type-2 fuzzy hypergraph. Jung-Heum Yon, Yong-Taek Kim, Jae-Yong Seo and Hong-Tae Jeon design an efficient neural network called dynamic multidimensional wavelet neural network. It can perform an effective dynamic mapping with less dimensions of the input signal. These features show one way to compensate the weakness of the diagonal recurrent neural network and feedforward wavelet neural network. Yigon Kim, Yang Hee Jung and Young Chel Bae propose a new method for diagnosis of insulation aging using wavelet. It measures the partial discharge on-line from data acquisition system and analyses it using wavelet to acquire 21) patterns. They design a neuro-fuzzy model that diagnoses an electrical equipment using the data. Byung-Jae Choi, Seong-Woo Kwak and Byung Kook Kim develop an adaptive fuzzy logic controller. A sole input fuzzy variable is used to simplify the design procedure and the switching hyperplane of sliding mode control is used to improve the adaptability. Myung-Geun Chun, Keun-Chang Kwak and Jeong-Woong Ryu show an efficient fuzzy rule generation scheme for adaptive network-based fuzzy inference system using the conditional fuzzy c-means and fuzzy equalization methods. They apply this method to the truck backer-upper control and Box-Jenkins modeling problem. Daijin Kim proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Twostage classification method is used. All data are classified by using the lower approximation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. Min-Soeng Kim, Sun-Gi Hong and Ju-Jang Lee incorporate the Q-learning algorithm into the fuzzy logic controller. Modified fuzzy rule is used for the incorporation. As a result, a fuzzy logic controller is obtained that can learn through experience. Dong Hwa Kim designs a new 2-DOF PID controller and applies it to the operating data based transfer function of Gun-san Gas turbine in Korea. We hope that this issue can be helpful to readers and we appreciate professor Kaoru Hirota for his interest and support for the publication.
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Saatchi, Reza. "Fuzzy Logic Concepts, Developments and Implementation." Information 15, no. 10 (2024): 656. http://dx.doi.org/10.3390/info15100656.
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Over the past few decades, the field of fuzzy logic has evolved significantly, leading to the development of diverse techniques and applications. Fuzzy logic has been successfully combined with other artificial intelligence techniques such as artificial neural networks, deep learning, robotics, and genetic algorithms, creating powerful tools for complex problem-solving applications. This article provides an informative description of some of the main concepts in the field of fuzzy logic. These include the types and roles of membership functions, fuzzy inference system (FIS), adaptive neuro-fuzzy inference system and fuzzy c-means clustering. The processes of fuzzification, defuzzification, implication, and determining fuzzy rules’ firing strengths are described. The article outlines some recent developments in the field of fuzzy logic, including its applications for decision support, industrial processes and control, data and telecommunication, and image and signal processing. Approaches to implementing fuzzy logic models are explained and, as an illustration, Matlab (version R2024b) is used to demonstrate implementation of a FIS. The prospects for future fuzzy logic developments are explored and example applications of hybrid fuzzy logic systems are provided. There remain extensive opportunities in further developing fuzzy logic-based techniques, including their further integration with various machine learning algorithms, and their adaptation into consumer products and industrial processes.
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Manisha Shinde-Pawar, Jagadish Patil, Alok Shah, and Prasanna Rasal. "DEVELOPMENT OF FUZZY INFERENCE SYSTEM FOR COVID-19 DATA ANALYSIS." Journal of Pharmaceutical Negative Results 13, no. 4 (2022): 693–99. http://dx.doi.org/10.47750/pnr.2022.13.04.093.
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As COVID-19 Pandemic and related data is very recent. COVID-19 infected most of the population from entire globe with different impact. It disclosed the limitation of access of health and care resources. Various parameters like different symptoms, different existing health conditions, different age, diagnosis level and great uncertainties made the condition vaguer. Fuzzy can handle such vagueness and uncertainty of such voluminous data of patients and can support to medical stakeholders, experts, hospitals, pharmaceuticals etc. Fuzzy Logic is widely used to address so many uncertainties, incompleteness or imprecision. The current experiment implements Fuzzy Inference System for pattern identification and classification by applying fuzzy approach with Fuzzy Logic in R for performance improvement. This focuses on designing Fuzzy Rule base, Model and inference for COVID 19 data analysis.
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Hasanah, Nur, and Retantyo Wardoyo. "Purwarupa Sistem Pakar dengan Mamdani Product untuk Menentukan Menu Harian Penderita DM." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 7, no. 1 (2013): 45. http://dx.doi.org/10.22146/ijccs.3051.
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AbstrakPada 2025 diperkirakan 12,4 juta orang yang mengidap Diabetes Melitus (DM) di Indonesia. Perencanaan makan merupakan salah satu pilar dalam pengelolaan DM. Sistem pakar dapat berfungsi sebagai konsultan yang memberi saran kepada pengguna sekaligus sebagai asisten bagi pakar. Logika fuzzy fleksibel, memiliki kemampuan dalam proses penalaran secara bahasa dan memodelkan fungsi-fungsi matematika yang kompleks. Penelitian ini bertujuan menerapkan metode ketidakpastian logika fuzzy pada purwarupa sistem pakar untuk menentukan menu harian. Manfaat penelitian ini adalah untuk mengetahui keakuratan mesin inferensi Mamdani Product. Pendekatan basis pengetahuan yang digunakan pada sistem pakar ini adalah dengan Rule-Based Reasoning. Proses inferensi pada sistem pakar menggunakan logika fuzzy dengan mesin inferensi Mamdani Product. Fuzzifier yang digunakan adalah Singleton sedangkan defuzzifier yang digunakan adalah Rata-Rata Terpusat. Penggunaan kombinasi Singleton fuzzifier, mesin inferensi Product dan defuzzifier Rata-Rata Terpusat yang digunakan pada sistem pakar dapat diterapkan untuk domain permasalahan yang dibahas. Meskipun demikian, terdapat kemungkinan Singleton fuzzifier tidak dapat memicu beberapa atau semua aturan. Jika semua aturan tidak dapat dipicu maka tidak dapat disimpulkan kebutuhan kalori hariannya. Kata kunci— sistem pakar, logika fuzzy, mamdani product, diabetes, menu AbstractIt is predicted that 12.4 million people will suffer from Diabetes Mellitus (DM) in Indonesia in 2025. Menu planning is one of the important aspects in DM management. Expert system can be used as a consultant that gives suggestion to users as well as an assistant for experts. Fuzzy logic is flexible, has the ability in linguistic reasoning and can model complex mathemathical functions. This research aims to implement fuzzy logic uncertainty method into expert sistem prototype to determine diabetic daily menu. The advantage is to find out the accuracy of Mamdani Product inference engine. The knowledge-based approach in this expert system uses Rule-Based Reasoning. The inference process employs fuzzy logic making use of Mamdani Product inference engine. The fuzzifier used is Singleton while defuzzifier is Center Average. The combination of Singleton fuzzifier, Mamdani Product inference engine and Center Average defuzzifier that is used can be applied in the domain of the problem under discussion. In spite of the case, there is possibility that Singleton fuzzifier can’t trigger some or all of the rules. If all of the rules can’t be triggered then the diabetic daily menu can’t be concluded. Keyword— expert system, fuzzy logic, mamdani product, diabetes, menu
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Muhammad Saqlain, Kashaf Naz, Kashf Gaffar, and Muhammad Naveed Jafar. "Fuzzy Logic Controller." Scientific Inquiry and Review 3, no. 3 (2019): 16–29. http://dx.doi.org/10.32350/sir.33.02.
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In this research paper, the impact of water pH on detergent was measured by constructing a Fuzzy Logic Controller (FLC) based on Intuitionistic Fuzzy Numbers (IFNs) by incorporating three linguistic inputs and one output as taken by Saeed. M. et al. [1]. The inference process was carried out using MATLAB fuzzy logic toolbox and the results were compared with FLC based on fuzzy numbers. The objective of the study was the comparison of FLC based on intuitionistic and fuzzy numbers. The results showed that FLC based on IFNs is approximately the same but has more precise values. So, IFNs based FLC can be used in the Instinctive Laundry System.
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Damayanti, Dela Rista, Suntoro Wicaksono, M. Faris Al Hakim, Jumanto Jumanto, Subhan Subhan, and Yahya Nur Ifriza. "Rainfall Prediction in Blora Regency Using Mamdani's Fuzzy Inference System." Journal of Soft Computing Exploration 3, no. 1 (2022): 62–69. http://dx.doi.org/10.52465/joscex.v3i1.69.
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In the case study of weather prediction, there are several tests that have been carried out by several figures using the fuzzy method, such as the Tsukamoto fuzzy, Adaptive Neuro Fuzzy Inference System (ANFIS), Time Series, and Sugeno. And each method has its own advantages and disadvantages. For example, the Tsukamoto fuzzy has a weakness, this method does not follow the rules strictly, the composition of the rules where the output is always crisp even though the input is fuzzy, ANFIS has the disadvantage of requiring a large amount of data. which is used as a reference for calculating data patterns and the number of intervals when calculating data patterns and Sugeno has the disadvantage of having less stable accuracy results even though some tests have been able to get fairly accurate results. In research on the implementation of the Mamdani fuzzy inference system method using the climatological dataset of Blora Regency to predict rainfall, it can be concluded as follows: (1) The fuzzy logic of the Mamdani method can be used to predict the level of rainfall in the city of Blora by taking into account the factors that affect the weather, including temperature, wind speed, humidity, duration of irradiation and rainfall. (2) Fuzzy logic for prediction with uncertain input values is able to produce crisp output because fuzzy logic has tolerance for inaccurate data. (3) The results of the accuracy of calculations using the Mamdani fuzzy inference system method to predict rainfall in Blora Regency are 66%.
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TRILLAS, E. "ON LOGIC AND FUZZY LOGIC." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 01, no. 02 (1993): 107–37. http://dx.doi.org/10.1142/s0218488593000073.
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This paper mainly consists of a review of some basic tools of Inexact Inference, its reduction to classical logic and its cautious use of Fuzzy Logic. Those tools are the concept of Conditional Relation, its greatest case of Material Conditional and the concept of Logical-States as possible worlds of "true" elements. Some recent results characterizing Monotonic Preorders are also introduced, in both the Classical and Fuzzy cases. Everything lies on the semantic level of Logic and is presented in a naive mathematical style.
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Mohd Safar, Noor Zuraidin, Azizul Azhar Ramli, Hirulnizam Mahdin, David Ndzi, and Ku Muhammad Naim Ku Khalif. "Rain prediction using fuzzy rule based system in North-West malaysia." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (2019): 1564. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1564-1573.
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<span>The warm and humid condition is the characteristic of Malaysia tropical climate. Prediction of rain occurrences is important for the daily operations and decisions for the country that rely on agriculture needs. However predicting rainfall is a complex problem because it is effected by the dynamic nature of the tropical weather parameters of atmospheric pressure, temperature, humidity, dew point and wind speed. Those parameters have been used in this study. The rainfall prediction are compared and analyzed. Fuzzy Logic and Fuzzy Inference System can deal with ambiguity that often occurred in meteorological prediction; it can easily incorporate with expert knowledge and empirical study into standard mathematical. This paper will determine the dependability of Fuzzy Logic approach in rainfall prediction within the given approximation of rainfall rate, exploring the use of Fuzzy Logic and to develop the fuzzified model for rainfall prediction. The accuracy of the proposed Fuzzy Inference System model yields 72%</span>
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Li, Yinghao, and Jawis M N. "Modeling performance evaluation in badminton sports: a fuzzy logic approach." Salud, Ciencia y Tecnología - Serie de Conferencias 3 (June 30, 2024): 986. http://dx.doi.org/10.56294/sctconf2024986.
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Spectators and many young students have flocked to badminton matches in recent years. Badminton practice has received a lot of media coverage. The current state of badminton evaluation methods is lacking in reliability. This article's overarching goal is to examine the many applications of fuzzy logic in badminton performance evaluation and improvement. Data on the badminton technique's flexion and extension phases are mapped into the suggested model using a fuzzy inference system (FIS). This study suggests a fuzzy logic-based badminton-specific objective fuzzy inference system (Bmt-FIS) to evaluate team sports. Despite the gravity of the situation, decisions involving performance reviews often use subjective data. These common decision-making problems may be realistically addressed by fuzzy logic models. Fuzzy logic has the potential to be an effective tool in situations where both quantitative and qualitative data interpretation are allowed. To do this, it accounts for the inherent variability in athletic performance by taking into consideration the 'hazy' or 'uncertain' limitations of data. By taking limitations into account, a rule-based approach makes performance evaluation more precise. Here, a fuzzy inference system (FIS) uses the input variables to evaluate the student's performance. While data mining approaches have been studied, the adaptive neural fuzzy method outperforms others because of its exceptional accuracy.This method eloquently and clearly conveys the many levels of integrity and ambiguity. Also, fuzzy logic may be a great tool for evaluating badminton skills. This foundational study connects the dynamic realm of sports with static measures.
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Wyrwoł, Bernard, and Edward Hrynkiewicz. "Decomposition of the fuzzy inference system for implementation in the FPGA structure." International Journal of Applied Mathematics and Computer Science 23, no. 2 (2013): 473–83. http://dx.doi.org/10.2478/amcs-2013-0036.
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The paper presents the design and implementation of a digital rule-relational fuzzy logic controller. Classical and decomposed logical structures of fuzzy systems are discussed. The second allows a decrease in the hardware cost of the fuzzy system and in the computing time of the final result (fuzzy or crisp), especially when referring to relational systems. The physical architecture consists of IP modules implemented in an FPGA structure. The modules can be inserted into or removed from the project to get a desirable fuzzy logic controller configuration. The fuzzy inference system implemented in FPGA can operate with a much higher performance than software implementations on standard microcontrollers.
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Darasing Ramrao Solanke, Suhas Dnyandeo Pachpande, and Rajesh Dinkarrao Bhoyar. "On the implementation of linear position control using fuzzy logic approach." International Journal of Science and Research Archive 14, no. 2 (2025): 560–69. https://doi.org/10.30574/ijsra.2025.14.2.0373.
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This paper investigated a significant application of fuzzy set theory and fuzzy logic. The proposed work aimed to simulate Fuzzy Logic Controller (FLC) for linear position control. With this objective, an initiative is taken to design and simulate a Fuzzy Inference System (FIS) to control linear position in a computer-assisted environment. In this paper, the implementation of a Mamdani Fuzzy Inference System has been demonstrated with the application of controlling linear displacement. The design and simulation of conventional fuzzy logic controller (FLC) for a single input single output (SISO) system, is carried out. The system consists of a fuzzy logic controller that analyzes probable control situation based on the error distance and its error derivative. The controller determines the sufficient linear motion required to obtain the desired set-position while providing a smooth motion for the slider. The robustness of the proposed control scheme is verified by numerical simulation. The proposed scheme has better performance than the conventional method due to parameter variation and extraneous disturbance. To demonstrate its performance, the proposed control algorithm is applied to a real time position control system.
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Bibi, T., Y. Gul, A. Abdul Rahman, and M. Riaz. "LANDSLIDE SUSCEPTIBILITY ASSESSMENT THROUGH FUZZY LOGIC INFERENCE SYSTEM (FLIS)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W1 (September 30, 2016): 355–60. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w1-355-2016.
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Landslide is among one of the most important natural hazards that lead to modification of the environment. It is a regular feature of a rapidly growing district Mansehra, Pakistan. This caused extensive loss of life and property in the district located at the foothills of Himalaya. Keeping in view the situation it is concluded that besides structural approaches the non-structural approaches such as hazard and risk assessment maps are effective tools to reduce the intensity of damage. A landslide susceptibility map is base for engineering geologists and geomorphologists. However, it is not easy to produce a reliable susceptibility map due to complex nature of landslides. Since 1980s, several mathematical models have been developed to map landslide susceptibility and hazard. Among various models this paper is discussing the effectiveness of fuzzy logic approach for landslide susceptibility mapping in District Mansehra, Pakistan. The factor maps were modified as landslide susceptibility and fuzzy membership functions were assessed for each class. Likelihood ratios are obtained for each class of contributing factors by considering the expert opinion. The fuzzy operators are applied to generate landslide susceptibility maps. According to this map, 17% of the study area is classified as high susceptibility, 32% as moderate susceptibility, 51% as low susceptibility and areas. From the results it is found that the fuzzy model can integrate effectively with various spatial data for landslide hazard mapping, suggestions in this study are hope to be helpful to improve the applications including interpretation, and integration phases in order to obtain an accurate decision supporting layer.
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Susana, Susana, and Suharjito Suharjito. "Query Optimization Using Fuzzy Logic in Integrated Database." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 3 (2016): 637. http://dx.doi.org/10.11591/ijeecs.v4.i3.pp637-642.
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<p>Query optimization in integrated database can’t be separated from data processing method. In order to have faster query response time, a method to optimize queries is required. One of many methods that can be used for query optimization is using fuzzy logic with Tsukamoto inference system. Value set on each variable is defined membership functions and Tsukamoto inference system used in determining these rules or the terms of query results, then apply it into query method or query line structure. The application of fuzzy logic inference systems with Tsukamoto can accelerate query response time, and will have more significant difference when the amount of selected data is greater.</p>
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Rosynskyi, A. "Fuzzy logic inference algorithms utilization in the economic potential growth management system of the real estate development company." Ways to Improve Construction Efficiency 2, no. 50 (2023): 180–202. http://dx.doi.org/10.32347/2707-501x.2022.50(2).180-202.
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The article defines the need to introduce fuzzy logic inference algorithms into the economic potential growth management system of the real estate development company. The use of fuzzy inference algorithms in the study of influencing factors on the profitability of development projects as prerequisites for economic potential growth of the real estate development company is proposed. It was determined that the increase in profitability and economic potential growth of the development company are particularly affected by managerial decisions regarding the development projects’ pricing policy, the justification of which requires the processing of complex economic information using fuzzy logic algorithms. As a result of a selective study of the price policy of various similar development projects of multi-story residential buildings in Kyiv, the factors affecting the change in prices for primary residential real estate were identified and grouped. A fuzzy logic inference algorithm was developed for the system of identified impact factors, which consists of separate subsystems of fuzzy inference for each factor group (apartment, building and time impact subsystems), which allows obtaining additional information necessary for making informed management decisions regarding the economic potential growth of a real estate development company. The characteristics of the input and output variables terms membership functions for all impact subsystems were determined, as well as sets of rules (knowledge bases) of each subsystem were developed. The process of creating and implementing the developed algorithm in the Fuzzy Logic Designer environment of the MATLAB software complex is presented, as well as the possibilities of using the functionality of its environment in order to obtain dynamic numerical and graphic data (in particular, graphs and surfaces) for the analysis and research of the state, characteristics and trends of development company economic processes in real time. The expediency of fuzzy logic inference algorithms utilization in the economic potential growth management system of the real estate development company has been proven.
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Senthil Kumar, S. "Multimodal Biometric Technology Using Fuzzy Logic Decision and Fuzzy Inference System." Asian Journal of Computer Science and Technology 5, no. 2 (2016): 1–4. http://dx.doi.org/10.51983/ajcst-2016.5.2.1772.
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In this research paper I discuss about the challenges to implement secure personal identification protocols with biometric technology are increasing and the need for accurate human identification is higher than ever. Single modality biometric systems have to contend with a variety of problems such as noisy data, intra class variations, non-universality, spoof attacks, and distinctiveness. Some of these limitations can be addressed by deploying multimodal biometric systems that integrate multiple biometric modalities in a single scan to alleviate the challenges of a unimodal system. Performance in biometric verification is often affected by external conditions and variabilities. These are often related to mismatched conditions between enrolment and verification sessions, e.g. handsets/microphones for recording speech, cameras for capturing facial images and fingerprint readers. In addition, the user’s speech may vary according to ambient noise conditions, the speaker’s health (e.g. contracting a cold) or speaking styles. The user’s facial images may vary due to changes in backgrounds, illumination, head positions and expressions. While none of the biometrics alone can guarantee absolute reliability, they can reinforce one another when used jointly to maximize verification performance. This motivates multi-biometric (multimodal) authentication.
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Ghosh, Goldina, Sandipan Roy, and Ali Merdji. "A proposed health monitoring system using fuzzy inference system." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 234, no. 6 (2020): 562–69. http://dx.doi.org/10.1177/0954411920908018.
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Due to the busy schedule of every human being in today’s world, consciousness towards one’s health has become quite alarming. A person suffering from any chronic disease needs a gradual, regular and close monitoring to recover from the disease or to be under control. Because of heavy work pressure, anxiety, change of weather and location or due to some other causes, the effect of the diseases can turn up into an appalling state. Two vital aspects of human diseases are blood pressure (hypertension) and blood sugar imbalance. Hypertension is one of the complications of prolonged untreated diabetes. Other organs like kidney, eye and peripheral nerves are also involved. The various gradations of hypertension and diabetes are required to understand for the progression of the disease and to make plan for the treatment. So these two aspects are considered in this article. The idea is to develop a logic, which could be incorporated in a pocket friendly device in future that would generate an alarm whenever there is imbalance in blood sugar or blood pressure levels. The concept of the fuzzy inference rule and first-order logic is implemented to develop this study.
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Safira, Silky, and Wifra Safitri. "Analisa Persespsi Masyarakat Pendatang Terhadap Kearifan Lokal Metode Fuzzy Inference System (FIS) Mamdani." Jurnal KomtekInfo 6, no. 2 (2019): 188–97. http://dx.doi.org/10.35134/komtekinfo.v6i2.58.
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Fuzzy logic is considered capable of mapping input into output without ignoring existing factors. Fuzzy logic is very flexible and tolerant of existing data. By using fuzzy logic, a model will be produced from a system that is able to estimate the perceptions of immigrants to local wisdom. The factors that influence the determination of immigrants' perceptions of local wisdom with fuzzy logic are the attitude of immigrant communities. Society's socio-cultural life is shown by the many links to other social life, such as ideology, lifestyle, and economy. This means that changes in one socio-cultural life will affect other social and cultural lives. Therefore this system is made so that the public can know, study and examine the variety of local wisdom, examine the role of indigenous and immigrant people in preserving local wisdom and study the strategies of indigenous and immigrant populations in limiting conflict and so on by applying fuzzy mamdani methods that are expected to provide decisions good in responding to the perceptions of immigrants towards local wisdom in West Kinali Pasaman.
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Preye, Uguta Henry, and Onyejegbu Laeticia Nneka. "An Intelligent Fuzzy Logic System for Network Congestion Control." Circulation in Computer Science 2, no. 11 (2017): 23–30. http://dx.doi.org/10.22632/ccs-2017-252-69.
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Network congestion is a major problem in all network environments as such it calls for ways to manage this problem. In this paper, we propose a Fuzzy Regulator Effective Random Early Detection (FRERED) system, which is an intelligent fuzzy logic based controller technique for early stage congestion detection, at the router buffer in the networks. The proposed technique extends the Fuzzy-Based system in the Fuzzy Hybrid ERED algorithm by considering the delay variable in its inference system to ease the problem of parameter initialization and parameter dependency. Unlike the Fuzzy-Based controller in the existing Fuzzy Hybrid ERED system which uses two parameter settings in its inference system that is, the queue size and average queue length in computing the dropping probability of packets. The proposed technique uses the queue size, average queue length and the delay approximation as input variables in computing the packet drop probability. The applied fuzzy logic system yields an output that denotes a packet dropping probability, which in turn controls and prevents congestion in early stage. This was achieved after simulating the proposed technique and the existing Fuzzy-Based controller using Matlab. The results obtained shows that this approach results in less packet drops for about the same link utilization as the existing Fuzzy-Based controller. Therefore, this technique, generally, controls network congestion and improves network performance. The methodology used to achieve this is the object oriented methodology and JAVA programming language was used to develop the system.
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Islam, Md Azharul, and Md Sahadat Hossain. "Optimizing the wash time of the washing machine using several types of fuzzy numbers." Journal of Bangladesh Academy of Sciences 45, no. 1 (2021): 105–16. http://dx.doi.org/10.3329/jbas.v45i1.54432.
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In this paper we used various fuzzy numbers to calculate wash time for washing machines and provided various decisions after comparison among them. Again, this paper shows the importance of fuzzy logic control-based washing machine to get an appropriate wash time for the degree of dirt and the quantity of grease present on the clothes. This method is predicated on a fuzzy inference system. A fuzzy inference system using the Mamdani controller type is illustrated in this paper. From the utilization of fuzzy logic control, the machine can respond in several conditions. The simulation was done by MATLAB programming language.
 J. Bangladesh Acad. Sci. 45(1); 105-116: June 2021
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Abdullah Muadz Nadzir Azhar, Deden Pradeka, and Devi Aprianti Rimadhani Agustini. "Study Program Selection Recommendation System Using the Fuzzy Inference System Mamdani." Jurnal Sistem Cerdas 7, no. 1 (2024): 13–25. http://dx.doi.org/10.37396/jsc.v7i1.384.
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This study presents a recommendation system for selecting study programs using the Mamdani fuzzy inference system. With the aim of assisting prospective students in making informed decisions, the system evaluates various factors including talents, UTBK scores, and school exam grades. The research utilizes the Temu Bakat test to assess talents and applies fuzzy logic to map inputs to outputs. Fuzzy rules are formulated based on the evaluation of antecedents, and aggregation combines multiple rules into a single output. Defuzzification converts fuzzy outputs into clear recommendations. The system's effectiveness is demonstrated through testing on students at UPI Campus in Cibiru, resulting in personalized study program recommendations for each student.
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CHAI, YUANYUAN, and LIMIN JIA. "CHOQUET INTEGRAL–OWA BASED ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM WITH APPLICATION." International Journal of Computational Intelligence and Applications 10, no. 01 (2011): 15–34. http://dx.doi.org/10.1142/s1469026811002970.
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In order to solve the defects of consequent part expression in ANFIS model and several shortcomings in FIS, this paper presents a Choquet Integral–OWA based Fuzzy Inference System, known as AggFIS. This model has advantages in consequent part of fuzzy rule, universal expression of fuzzy inference operator and importance factor of each criteria and each rule, which is trying to establish fuzzy inference system that can fully reflect the essence of fuzzy logic and human thinking pattern. If we combine AggFIS with a feed forward-type neural network according to the basic principles of fuzzy neural network, we can obtain Choquet Integral–OWA based Adaptive Neural Fuzzy Inference System, which is named Agg-ANFIS. We apply this Agg-ANFIS model into the evaluation of traffic level of service. The experimental results show that Choquet Integral–OWA based Adaptive Neural Fuzzy Inference System (Agg-ANFIS) is a universal approximator because of its infinite approximating capability by training and can be used in complex systems modeling, analysis and prediction.
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Kumar, Neerendra, and Zoltán Vámossy. "Robot navigation with obstacle avoidance in unknown environment." International Journal of Engineering & Technology 7, no. 4 (2018): 2410. http://dx.doi.org/10.14419/ijet.v7i4.14767.
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In this paper, a robot navigation model is constructed in MATLAB-Simulink. This robot navigation model make the robot capable for the obstacles avoidance in unknown environment. The navigation model uses two types of controllers: pure pursuit controller and fuzzy logic controller. The role of the pure pursuit controller is to generate linear and angular velocities to drive the robot from its current position to the given goal position. The obstacle avoidance is achieved through the fuzzy logic controller. For the fuzzy controller, two novel fuzzy inference systems (FISs) are developed. Initially, a Mamdani-type fuzzy inference system (FIS) is generated. Using this Mamdani-type FIS in the fuzzy controller, the training data of input and output mapping, is collected. This training data is supplied to the adaptive neuro-fuzzy inference system (ANFIS) to obtain the second FIS as of Sugeno-type. The navigation model, using the proposed FISs, is implemented on the simulated as well as real robots.
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Mr., Vinay Barod Ms. Shalini Modi Ms.Yamini Bhavsar Ms.Preetika Saxena y. "COMPARISON OF MAMDANI & SUGENO TYPE FUZZY INFERENCE SYSTEM ON ENROLLMENT DATASETS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 794–12. https://doi.org/10.5281/zenodo.51907.
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As the Application of Computer is increasing day by day. Computers are also used in the field of Business Analysis and Forecasting. There is various approaches in the field of forecasting. Fuzzy logic is the branch of Soft Computing that is widely used in the field of forecasting. Fuzzy Inference System is used to map inputs to outputs. In this Paper both the Mamdani and Sugeno model of Fuzzy Inference System are compared based on Performance and Error Rate.
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Azar, Ahmad Taher. "Overview of Type-2 Fuzzy Logic Systems." International Journal of Fuzzy System Applications 2, no. 4 (2012): 1–28. http://dx.doi.org/10.4018/ijfsa.2012100101.
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Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 fuzzy set (T1FS) does not capture uncertainty in its manifestations when it arises from vagueness in the shape of the membership function. Such uncertainties need to be depicted by fuzzy sets that have blur boundaries. The imprecise boundaries of a type-2 fuzzy set (T2FS) give rise to truth/membership values that are fuzzy sets in [0], [1], instead of a crisp number. Type-2 fuzzy logic systems (T2FLSs) offer opportunity to model levels of uncertainty which traditional fuzzy logic type1 struggles. This extra dimension gives more degrees of freedom for better representation of uncertainty compared to type-1 fuzzy sets. A type-1 fuzzy logic system (T1FLSs) inference produces a T1FS and the result of defuzzification of the T1FS, a crisp number, whereas a T2FLS inference produces a type-2 fuzzy set, its type-reduced fuzzy set which is a T1FS and the defuzzification of the type-1 fuzzy set. The type-reduced fuzzy set output gives decision-making flexibilities. Thus, FLSs using T2FS provide the capability of handling a higher level of uncertainty and provide a number of missing components that have held back successful deployment of fuzzy systems in decision making.
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Marakhimov, Avaz, and Abdushukur Abdullaev. "MODELS AND FUZZY MICROCLIMATE CONTROL SYSTEM IN THE STORAGE OF ARCHIV GE OF ARCHIVAL DOCUMEN AL DOCUMENTS." Technical science and innovation 2021, no. 3 (2021): 54–61. http://dx.doi.org/10.51346/tstu-02.21.3-77-0020.
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In this article, the main object of research is the creation of appropriate microclimatic conditions to ensure reliable and high-quality storage of archival documents, as well as automatic control of the optimal values of the main parameters of the external and internal environment that directly affect the quality of storage. To control the microclimate, three categories of models for automatic control of these parameters are considered separately in the archives: the “white box”, “black box” and “gray box " models. The results of the analysis of the advantages and disadvantages of the considered models are presented. The generalized structure of the microclimate management system is also given, as well as a list of controlled and changeable parameters of the microclimate management system of archives. It is proposed to use the fuzzy logic apparatus to create microclimate control systems in archival repositories, which allows synthesizing stable algorithms for its functioning in conditions of uncertainty. The specific steps that need to be performed when designing and using fuzzy inference systems and which are implemented based on the rules of fuzzy logic are listed. When designing and using fuzzy inference systems, it is necessary to observe certain stages that are implemented based on the rules of fuzzy logic. A generalized algorithm for forming a rule base with a technique for implementing the fuzzy inference procedure is presented. The tasks that need to be solved when designing a fuzzy control system are indicated. A system of automatic temperature control in archival repositories with a fuzzy logic controller is presented.
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Khan, Ameer Hamza, and Muhammad Sajid. "Building a Cost Modeling System using Fuzzy Logic for Sugar Industry." International Journal of Advanced Natural Sciences and Engineering Researches 7, no. 7 (2023): 51–55. http://dx.doi.org/10.59287/ijanser.1335.
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To understand the effects of cost factors on the overall production cost, the study's goal was todevelop a fuzzy logic-based cost modelling system for the sugar sector. The information is gathered fromsugar factories in Pakistan. Utilising a multistage fuzzy inference approach, the model is created. To analysethe cost of producing sugar, the model is verified using cost factors including the cost of raw materials, thecost of labour, and the cost of distribution. The main goal of the study was to ascertain how theseuncontrollable cost factors affected the price of producing sugar. The sub-cost components were used toanalyse the cost variables independently. For each cost variable, a different fuzzy inference technique wasused to interpret their response. Then a complete Mamdani inference system for manufacturing cost wascreated. The final inference system's input was derived from the results of the sub inference systems. Inorder to design the system to assess the effects of specified cost drivers on the production cost, a total ofthree input variables, one output variable, and twenty-seven if-then rules were established. The createdfuzzy logic-based system can assess the cost of producing sugar while taking uncertainty into consideration.As a result, the created system's offer of a cost estimating model that makes it easier to choose outcomesthat are cost-effective is a major contribution.
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Żurek, Józef, Norbert Grzesik, and Jakub Kurpas. "Selected Aircraft Throttle Controller With Support Of Fuzzy Expert Inference System." Journal of KONBiN 30, no. 1 (2014): 87–97. http://dx.doi.org/10.2478/jok-2014-0017.
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Abstract The paper describes Zlin 143Lsi aircraft engine work parameters control support method – hourly fuel flow as a main factor under consideration. The method concerns project of aircraft throttle control support system with use of fuzzy logic (fuzzy inference). The primary purpose of the system is aircraft performance optimization, reducing flight cost at the same time and support proper aircraft engine maintenance. Matlab Software and Fuzzy Logic Toolbox were used in the project. Work of the system is presented with use of twenty test samples, five of them are presented graphically. In addition, system control surface, included in the paper, supports system all work range analysis.
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Rahman, Sam Matiur, Mohammad Fazle Rabbi, Omar Altwijri, et al. "Fuzzy logic-based improved ventilation system for the pharmaceutical industry." International Journal of Engineering & Technology 7, no. 2 (2018): 640. http://dx.doi.org/10.14419/ijet.v7i2.9985.
Full textAbstract:
Indoor air quality in pharmaceutical industry plays a vital role in the production and storing of medicine. Stable indoor environment including favorable temperature, humidity, air flow and number of microorganisms requires consistent monitoring. This paper aimed to develop a fuzzy logic-based intelligent ventilation system to control the indoor air quality in pharmaceutical sites. Specifically, in the proposed fuzzy inference system, the ventilation system can control the air flow and quality in accordance with the indoor temperature, humidity, air flow and microorganisms in the air. The MATLAB® fuzzy logic toolbox was used to simulate the performance of the fuzzy inference system. The results show that the efficiency of the system can be improved by manipulating the input-output parameters according to the user’s demands. Compared with conventional heating, ventilation and air-conditioning (HVAC) systems, the proposed ventilation system has the additional feature of the existence of microorganisms, which is a crucial criterion of indoor air quality in pharmaceutical laboratories.