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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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Istiadi, Istiadi, Emma Budi Sulistiarini, Rudy Joegijantoro, Anik Vega Vitianingsih, and Affi Nizar Suksmawati. "Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious Diseases." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 6 (2022): 1047–56. http://dx.doi.org/10.29207/resti.v6i6.4656.
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E-learning and expert systems can be implemented for learning in the health sector. Through the e-learning system, prospective health workers can analyze problems by exploring the material in the system. However, material learning alone is less effective, so case study-based learning using an expert system is needed to strengthen understanding. The research applies an expert system to online learning to diagnose several infectious diseases. The disease diagnosis process uses the backward chaining method and the Mamdani fuzzy inference system. The fuzzy Mamdani inference system determines the intensity of disease severity so that appropriate treatment recommendations can be made. The test findings on 15 test datasets yielded a backward chaining accuracy value of 100%. Three test scenarios were used to establish the test using the Mamdani fuzzy inference method. Scenario 1: Testing with the Center of Gravity defuzzification and Fuzzy Mamdani Min inference system Tests employing the Fuzzy Mamdani Min inference method and center average defuzzification are used in Scenario 2. Scenario 3 involves testing using the Fuzzy Mamdani Product Inference System with Center Average Defuzzification. The average outcome for the intensity of disease severity utilizing the Fuzzy Mamdani Min inference system with Center of Gravity defuzzification was greater than that of the two test scenarios that were suggested, which was 49.43%.
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Manek, Siprianus Septian, Grandianus Seda Mada, and Yoseph P. K. Kelen. "PRE-ECLAMPSIA DIAGNOSIS EXPERT SYSTEM USING FUZZY INFERENCE SYSTEM MAMDANI." Jurnal Techno Nusa Mandiri 20, no. 2 (2023): 80–88. http://dx.doi.org/10.33480/techno.v20i2.4622.
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Various institutions utilize computer information systems to analyze and process data. An expert system is an information system that is used to help analyze and determine decisions on a problem based on rules determined by experts. This research focuses on creating a prototype expert system for diagnosing pre-eclampsia or pregnancy poisoning in pregnant women based on measuring blood pressure and checking proteinuria. The existing data is then analyzed using the Mamdani system's fuzzy inference method. Supporting theory regarding the fuzzy inference system of Mamdani, pre-eclampsia and its examination indicators will be used as a basis for creating this expert system prototype. The data used were secondary data on preeclampsia patients in the form of medical records of blood pressure measurements, proteinuria examinations and doctor diagnoses of preeclampsia patients at two Regional General Hospitals (RSUD), namely Atambua and Kefamenanu, totaling 20 samples. The interface or user interface of this prototype system is made as simple as possible so that it can be operated by all ordinary people. The programming language used is Visual Basic (VB) with the Visual Studio 2010 developer application. The initial prototype of this system will continue to be developed until it can become a Information systems or real applications used in hospitals. The results of this research are that the expert system for diagnosing preclampsia can be used well and easily by hospital staff and show congruence between the system diagnosis results and the diagnosis results from obstetricians or experts in the 20 processed data.
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LEE, KEON-MYUNG, and HYUNG LEE-KWANG. "FUZZY INFORMATION PROCESSING FOR EXPERT SYSTEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 01 (1995): 93–109. http://dx.doi.org/10.1142/s0218488595000098.
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This paper investigates the problems incurred when fuzzy values and certainty factors are used in rule-based knowledge representation. It proposes several measures for evaluating the satisfaction degree of fuzzy matching, fuzzy comparison and interval inclusion occurring in the course of inference for such knowledge representation. It introduces an inference method for such knowledge representation. In addition, it suggests a strategy for flexibly using and managing both conventional rules and fuzzy production rules in rule-based systems. Finally a fuzzy expert system shell, called FOPS5, designed to accommodate fuzzy information processing, is presented in consideration of the proposed methods.
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Latafat Gardashova, Aytaj Ismayilova, Latafat Gardashova, Aytaj Ismayilova, and Gulay Tarverdiyeva Gulay Tarverdiyeva. "EXPERT SYSTEM FOR DENTAL DISEASES." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 31, no. 08 (2023): 23–30. http://dx.doi.org/10.36962/pahtei31082023-23.
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The objective of an expert system in the field of medicine is to support doctors during the diagnosis process. Any software that is capable of drawing conclusions and making decisions based on the data stored in its database can be called an "expert system." Expert systems are widely employed in many industries, including the health sector. There are numerous types of dental ailments in the field of dentistry. Few symptoms were employed in the existing methods for dental diagnostics. A diagnosis in dentistry requires more than a few symptoms. The aim of this chapter is to analyse a medical expert system based on fuzzy rules that is used for the diagnosis of dental illnesses. Fuzzy inference based on a possibility metric and information extraction based on fuzzy clustering are both used in the modeling. The relevant parameters for dentistry were determined in the initial modeling phase of the system using clinical data. The evaluation of the dental variables based on soft computing will be carried out in the next stage. The third step introduces applied fuzzy inference based on possibility measures and provides examples to support it. The case data gathered from 100 patients makes up the knowledge foundation of the modeled system. Keywords: dental disease, big data, expert system, fuzzy clustering
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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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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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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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Hirota, Kaoru, MingQiang Xu, Yasufumi Takama, and Hajime Yoshino. "Implementation of Fuzzy Legal Expert System FLES." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 6 (2000): 421–27. http://dx.doi.org/10.20965/jaciii.2000.p0421.
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A fuzzy legal expert system FLES based on a fuzzy Housdorff similarity measure is implemented. The reasoning approach in this system includes the fuzzy case-based reasoning that is composed of knowledge representation, retrieval, and inference. The proposed approaches are illustrated by the experiments, where the target law is CISG (United Nation Convention on Contract for the International Sale of Goods).
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PANIAGUA MEDINA, JUAN JOSE, Sarahí Camargo Carmona, ANA DINORA GUZMAN CHAVEZ, and Everardo Vargas Rodríguez. "FUZZY INFERENCE SYSTEM FOR DIAGNOSING STRESS AND ITS EVOLUTION IN LAYING HENS." DYNA NEW TECHNOLOGIES 10, no. 1 (2023): [11P.]. http://dx.doi.org/10.6036/nt10901.
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In this work a fuzzy inference system design to estimate if laying hens present some level of stress without an expert intervention, as a poultry veterinarian, is presented. Additionally, in small farms usually hens diagnosed as stressed are isolated during some days until they are recovered. Moreover, isolated hens are diary examined by the expert to diagnose if the stress has disappeared. Here, it is important to point out that experts usually are unable to estimate the number of days that the hen will need to be kept in isolation until they are recovered. Therefore, as an additional advantage of the proposed FIS is that it can estimate this parameter. In this way it is shown that with the proposed FIS it was possible to determine with an accuracy of 98.62% the stress level and of 90.41% the number of days that the hen will require in isolation. Furthermore, the confusion matrices for these two variables shown that in most of the cases the incorrect estimations have an error of ±1 stress level and ±1 days of isolation. Finally, it is shown that it is feasible to implement this FIS because of the inferences are performed with variables that can be easily measured or identified by non-experts in the hen stress diagnostic. Consequently, it can help to minimize the frequency of the expert intervention in the process. Keywords: Fuzzy Logic, Inference System, Laying Hens, Stress, Classification.
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Imanov, Elbrus, Orhan Ozkilic, and Gunay Imanova. "Flight information system by using fuzzy expert inference." Procedia Computer Science 120 (2017): 304–10. http://dx.doi.org/10.1016/j.procs.2017.11.243.
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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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Tian, Miao. "Diagnose Expert System of Engine Based on Fuzzy Neural Network." Advanced Materials Research 588-589 (November 2012): 1472–75. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1472.
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Engine has a high chance of failure, it usually accounts for about 40% of vehicle failures. Study expert system of engine fault diagnosises that it can locate fault timely and accurately, and enhance efficiency. However, the traditional expert system has shortcomings so as inefficient inference and poor self-learning capability. The fuzzy logic and traditional neural networks are combined to form fuzzy neural networks, they are established a model of fuzzy neural network (FNN) of fault diagnosis, and that the model is applied to engine fault diagnosis, complementary advantages, to effectively enhance efficiency of inference and self-learning ability, its performance is higher than the traditional BP network.
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Rizal, Farhan Syah, Lita Karlitasari, and Halimah Tus Sadiah. "EXPERT SYSTEM DIAGNOSES VARICOCHEL DISEASE USING MAMDANI'S FUZZY LOGIC ALGORITHM METHOD." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 21, no. 2 (2024): 44–54. http://dx.doi.org/10.33751/komputasi.v21i2.9810.
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Varicoceles form when valves in the veins that run along the spermatic cord (the structure that suspends the testicles in the scrotum) prevent blood from flowing properly. Most varicocele cases occur during puberty, from the age of 15 to 25 years. If there are symptoms that are not prolonged, treatment is not necessary. However, if a varicocele causes pain, shrinkage of the testicles, impaired fertility, or swelling, surgery will be performed. Symptoms of this disease are similar to hemorrhoids and bladder stones, so it takes an expert. In the research conducted on the Expert System Application for Diagnosing Hyperthyroid Disease with the Mamdani Fuzzy Logic Inference Method, it can be concluded that the expert system can store expert knowledge from experts in solving problems diagnosing hyperthyroid disease while Mamdani fuzzy inference is used for knowledge processing in order to obtain a more accurate diagnosis conclusion. definitely with accuracy . Making the application is expected to make it easier for people to get information without having to wait for the presence of a doctor/expert for varicocele disease, and is expected to reduce or even solve existing problems.
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Syarief, Mohammad, Imamah Imamah, Husni Husni, and Akhmad Tajuddin Tholaby MS. "Mobile expert for Tobacco Disease Identification Using The Fuzzy Inference System Tsukamoto." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 6, no. 1 (2020): 73. http://dx.doi.org/10.26418/jp.v6i1.37258.
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Madura Island is a producer of aromatics tobacco known as Madura Tobacco. One type of Madura tobacco that has superior quality is tobacco Campalok. This tobacco is only in the village of Bakeong Guluk-Guluk district of Sumenep. Its price for each kilogram can penetrate up to two million rupiahs. But failing to harvest due to illness or pests can decrease the quality and price of tobacco Campalok, while the access to consult the agricultural experts in Sumenep district is far enough so the public difficulty getting information faster on tobacco disease treatment. This is the underlying research on the expert system for the identification of diseases in the Android-based tobacco crop.This Expert System was developed by utilizing Android-based mobile technology using the Fuzzy Inference System Tsukamoto method. Farmers who will use this application only enter the characteristics of tobacco leaves that are exposed to pests then the expert system will provide a way of overcoming the pest disease based on the expertise of agricultural experts in Sumenep district Using the Fuzzy method. The result of this research showed that 8 from 10 of diseases were successfully detected by the application so that the accuracy of this application compared to the human expert system is 80 %.
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Song, Xu Dong, Wei Shao, Zhan Zhi Qiu, and Yan Xia Chen. "Study on Fuzzy Inference Method for Fault Diagnosis Expert System." Advanced Materials Research 658 (January 2013): 639–42. http://dx.doi.org/10.4028/www.scientific.net/amr.658.639.
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In this paper, a new fuzzy inference method for fault diagnosis expert system is proposed. Firstly, fault antecedents were processed by lexical analysis including removing redundant vocabulary and word segmentation. Secondly, for the purpose of improving the precision of the inference, we used the membership degree, and defined the confidence level of antecedent and the confidence level of conclusion and the closeness degree of the knowledge base rules. Finally we gave the reasoning processes and algorithms.
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Rana, Anil, and Emosi V. M. Koroitamana. "Measuring maintenance activity effectiveness." Journal of Quality in Maintenance Engineering 24, no. 4 (2018): 437–48. http://dx.doi.org/10.1108/jqme-11-2016-0061.
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Purpose The purpose of this paper is to provide a framework for measuring the imprecise and subjective “effectiveness” of a major maintenance activity. Such a measure will not only bring objectivity in gauging the effectiveness of maintenance task carried out by the workforce without any intervention from an expert but also help in measuring the slow degradation of the performance of the concerned major equipment/system. Design/methodology/approach The paper follows a three-step approach. First, identify a set of parameters considered important for estimating the maintenance activity effectiveness. Second, generate a set of data using expert opinions on a fuzzy performance measure of maintenance activity effectiveness (output). Also, find an aggregated estimate of the effectiveness by analysing the consensus among experts. This requires using a part of the “fuzzy multiple attribute decision making” process. Finally, train a neuro-fuzzy inference system based on input parameters and generated output data. Findings The paper analysed major maintenance activity carried out on diesel engines of a power plant company. Expert opinions were used in selection of key parameters and generation of output (effectiveness measure). The result of a trained adaptive neuro-fuzzy inference system (ANFIS) matched acceptably well with that aggregated through the expert opinions. Research limitations/implications In view of unavailability of data, the method relies on training a neuro-fuzzy system on data generated through expert opinion. The data as such are vague and imprecise leading to lack of consensus between experts. This can lead to some amount of error in the output generated through ANFIS. Originality/value The originality of the paper lies in presentation of a method to estimate the effectiveness of a maintenance activity.
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Asrol, Muhammad, Endang Djuana, Christian Harito, Arief S. Budiman, and Fergyanto E. Gunawan. "Developing an Inference Engine for Estimating State of Charge of the Lead Acid Battery." IOP Conference Series: Earth and Environmental Science 1169, no. 1 (2023): 012001. http://dx.doi.org/10.1088/1755-1315/1169/1/012001.
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Abstract Lead acid battery efficiency is affected by huge uncertainty features. An inference engine is required to monitor the uncertainty of the battery state of charge. The objective of the research is to design an inference system to predict the lead acid battery state of charge. A Relief algorithm and Pearson correlation were applied to pre-process the real-world dataset. A fuzzy inference system was adopted to design the inference engine of the state of charge. This research found four main features which had significant impact to lead acid state of charge, including: export power, temperature, volt per cell and ampere. These features had different directions of correlation and furtherly set as inference system’s output. This research had successfully developed an inference engine for lead acid state of charge with Mamdani fuzzy type and centroid defuzzification. In the future, it needs expert validation of the developed rules in the inference system.
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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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Khalyasmaa, Alexandra, Artem Aminev, and Dmitry Bliznyuk. "Equipment State Assessment System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)." Applied Mechanics and Materials 792 (September 2015): 243–47. http://dx.doi.org/10.4028/www.scientific.net/amm.792.243.
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The paper is dedicated to analyze the modern expert systems to assess the technical condition of power stations and substations high-voltage equipment. The main problems of modern expert systems and their possible solutions are determined. As the structure and their basic construction principles are considered. Also this paper proposes an algorithm for the expert system model using fuzzy inference on the basis of technical diagnostics and tests. As a case study of assessment of power transformers state based on dissolved gas analysis in the oil is presented.
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Iringin Telaumbanua, Dian. "Rhabdomyosarcoma Diagnosis Expert System Applying Adaptive Neuro Fuzzy Inference System Method." Management of Information System Journal 3, no. 1 (2024): 18–22. https://doi.org/10.47065/mis.v3i1.1915.
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Rhabdomyosarcoma (RMS) is a cancer that originates from striated muscles (muscles that move the body). Is a cancer that is formed from the soft tissues in the body, such as muscles, fat cells, bones, joints and blood vessels. This cancer grows and develops in any part of the body and Rhabdomyosarcoma disease is most commonly found in children. To get an accurate diagnosis, it requires an expert system using the Adaptive Neuro Fuzzy Inference System (ANFIS) method. Rhabdomyosarcoma disease was designed using the Visual Basic Net 2008 programming language and using the MYSQL database, hoping that it could become an alternative tool and bring positive impacts for all its users.
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Celestine, Okpokpong Nathaniel Ntebong, Umar Farouk Ibn Abdulrahman, and Itoro Akpabio. "Fuzzy Expert Framework for Diagnosis of Typhoid." Circulation in Computer Science 2, no. 9 (2017): 13–16. http://dx.doi.org/10.22632/ccs-2017-252-53.
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Typhoid fever is a disease that is caused by bacteria called salmonella typhi. It is also known as Enteric fever, Typhoid fever is been characterized by high fever, constipation, diarrhoea, abdominal pain, etc. It is often treatable when diagnosed early, but if left untreated could lead to other medical complications like intestinal haemorrhaging which may require major surgeries and could even lead to death. This paper proposes a method of diagnosis of Typhoid Fever using Fuzzy Logic. The system was built with twenty input membership functions, one output membership function and about two hundred inference rules which was simulated with MATLAB R2013 and therefore 97.5 % accuracy was obtained. The centroid method was used for the defuzzification. Although there are many systems in existence, this work is however based on the assumption that a system with a higher number of inference rules will make diagnosis a better.
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Lee, H. K., K. D. Oh, D. H. Park, J. H. Jung, and S. J. Yoon. "Fuzzy expert system to determine stream water quality classification from ecological information." Water Science and Technology 36, no. 12 (1997): 199–206. http://dx.doi.org/10.2166/wst.1997.0448.
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Water quality classification for stream has been major tool for water quality management in Korea. This paper examines the application of the fuzzy inference mechanism to develop a fuzzy expert system for proper determination of WQCS from uncertain and imprecise ecological information. This study proposes a rule matrix composed of seven water quality grades, toxicity of water and rarity of cases. From this rule matrix, 30 rules for WQCS determination are generated. From the comparison of performance of the fuzzy expert system and the conventional expert system for the determination of class, toxicity, and rarity, it seems that the smoothly varying curves of WQCS determination from the fuzzy expert system represent our real-world experience more realistically than stepwise curves from the conventional expert system.
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Devi, U. Ramya, and K.Uma. "Diagnosis of Human Diseases with Fuzzy Expert System." International Journal of Fuzzy Mathematical Archive 14, no. 02 (2017): 191–98. http://dx.doi.org/10.22457/ijfma.v14n2a3.
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One of the major problems that both the developed and under-developed countries are facing is the difficulties of treating ill health People. Medical diagnosis usually involves careful examination of a patient to check the presence and strength of some features relevant to a suspected disease in order to take a decision whether the patient suffers from that disease or not. Fuzzy logic is a simple and effective technique that can be advantageously used for medical diagnosis of a wide range of diseases. Now a days fuzzy systems are being used successfully in an increasing number of application areas they use linguistic rules to describe it. The developed fuzzy expert system composed of four components which include the knowledge base, the fuzzification, the inference engine and defuzzification. This paper describe an aim to develop a fuzzy expert system for diagnosing human diseases.
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Zhang, Li Li, and Jiang Wei Chu. "Key Technologies Study for Automobile Fault Diagnosis Expert System." Advanced Materials Research 457-458 (January 2012): 913–20. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.913.
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Expert system for automobile fault diagnosis is an intelligent system, and its key technologies are knowledge acquisition, knowledge representation and inference strategy. Based on large collection of papers which on abroad or on home, some resolved methods have been presented in this paper, including improving of traditional method such as automotive acquisition of fault rules, combined knowledge representation and inference method diversification, applying of new theory or new technology such as case-base expert system, fuzzy-base expert system, neural network-base expert system and action-base expert system and so on. And then put forward to that intellectualization, cyberization and integration are the future development direction of automobile fault diagnosis expert system.
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Mai, Thi Nu, and Nguyen Hoang Phuong. "Improving the fuzzy expert system for diagnosing depressive disorders." Vietnam Journal of Science and Technology 60, no. 6 (2022): 1149–61. http://dx.doi.org/10.15625/2525-2518/16896.
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This paper presents an improving knowledge base and inference engine of a medical expert system for diagnosing depressive disorders. This medical expert system calls PORUL.DEP. PORUL.DEP’s knowledge base includes more than 850 positive rules. PORUL.DEP has been tested on more than 260 medical records of depressed patients. PORUL.DEP gives a correct diagnosis of more than 95% with light depressive disorder and without depressive disorder, but the remaining depressive disorders are not accurate. Average percent of more than 24 %. A new expert system, called STRESSDIAG, was developed on combining positive rules (for confirmation of conclusion) and negative rules (for exclusion of conclusion) for diagnosing depressive disorders. STRESSDIAG’s knowledge base consists of more than 850 positive rules of PORUL.DEP and more than 120 negative rules. Abelian group operation of Mycin is used to improve the inference engine based on fuzzy relations. STRESSDIAG gives the correct diagnosis of more than 76% with 4 depressive disorders types and without depressive disorders. Average percent of more than 82 %, up nearly 60% compared to PORUL.DEP.
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Masivi, Osée Muhindo. "Fuzzy Expert System Generalised Model for Medical Applications." Applied Computer Systems 24, no. 2 (2019): 128–33. http://dx.doi.org/10.2478/acss-2019-0016.
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Abstract Over the past two decades an exponential growth of medical fuzzy expert systems has been observed. These systems address specific forms of medical and health problems resulting in differentiated models which are application dependent and may lack adaptability. This research proposes a generalized model encompassing major features in specialized existing fuzzy systems. Generalization modelling by design in which the major components of differentiated the system were identified and used as the components of the general model. The prototype shows that the proposed model allows medical experts to define fuzzy variables (rules base) for any medical application and users to enter symptoms (facts base) and ask their medical conditions from the designed generalised core inference engine. Further research may include adding more composition conditions, more combining techniques and more tests in several environments in order to check its precision, sensitivity and specificity.
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Ashraf, Ather, Muhammad Akram, and Mansoor Sarwar. "Type-II Fuzzy Decision Support System for Fertilizer." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/695815.
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Type-II fuzzy sets are used to convey the uncertainties in the membership function of type-I fuzzy sets. Linguistic information in expert rules does not give any information about the geometry of the membership functions. These membership functions are mostly constructed through numerical data or range of classes. But there exists an uncertainty about the shape of the membership, that is, whether to go for a triangle membership function or a trapezoidal membership function. In this paper we use a type-II fuzzy set to overcome this uncertainty, and develop a fuzzy decision support system of fertilizers based on a type-II fuzzy set. This type-II fuzzy system takes cropping time and soil nutrients in the form of spatial surfaces as input, fuzzifies it using a type-II fuzzy membership function, and implies fuzzy rules on it in the fuzzy inference engine. The output of the fuzzy inference engine, which is in the form of interval value type-II fuzzy sets, reduced to an interval type-I fuzzy set, defuzzifies it to a crisp value and generates a spatial surface of fertilizers. This spatial surface shows the spatial trend of the required amount of fertilizer needed to cultivate a specific crop. The complexity of our algorithm isO(mnr), wheremis the height of the raster,nis the width of the raster, andris the number of expert rules.
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Zhou, Chun Hua, Yun Cheng Wang, and Chu Xiang Chen. "Fuzzy Evaluation Expert System for Undergraduate Comprehensive Quality Based on Agent." Advanced Materials Research 756-759 (September 2013): 2557–61. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2557.
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Aimed at the actual demand of current evaluation job for undergraduates, based on fuzzy set theory, a new thought to make comprehensive evaluation about undergraduates was raised by combining with Agent technology and artificial intelligence expert system technology in this paper. The mathematical model for evaluation was established via weighted fuzzy evaluation method and weighted fuzzy inference method based on reliability and realized by adopting Java language under Web environment, to gain professional conclusions and suggestions for undergraduate comprehensive evaluation.
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Alfiqri, Fahreza. "Microcontroller-Based Water Quality Monitoring System Implementation." Brilliance: Research of Artificial Intelligence 2, no. 2 (2022): 53–57. http://dx.doi.org/10.47709/brilliance.v2i2.1544.
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So far, Regional Drinking Water Companies (PDAMs) have used conventional methods by taking water samples, measuring all water quality parameters, and analyzing them one by one. In addition, the process of making conclusions on water quality has not been integrated so that it can cause misclassification of water quality and prolong the work. In this study, an expert system was designed to monitor water quality that works in real time so that it can be accessed anytime and anywhere. The water quality analysis process is carried out with a fuzzy classifier realized using Arduino Mega 2560. The fuzzy input variables include the pH value, total dissolved solids (TDS), and turbidity or turbidity. A fuzzy inference system is used to classify water quality into three classes, namely good (meets quality standards), ordinary, and bad (polluted). The expert system of success provides inference results with a 100% success percentage. The results of monitoring and water quality classification can be accessed online using the Internet of Things (IoT) ThingSpeak platform
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Jumadi, Jumadi, Wildan Budiawan Zulfikar, Dicky Andika Sulaeman, and Muhammad Ali Ramdhani. "Design of expert system for train operational feasibility with Tsukamoto fuzzy inference system." MATEC Web of Conferences 197 (2018): 03015. http://dx.doi.org/10.1051/matecconf/201819703015.
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Train is one of the most favorite mass transportation in the world. In 2017 train in Indonesia can bring about 341.605 passengers to many destinations. PT. Kereta Api Indonesia is a state owned enterprise that has responsibility to make sure that train is safe and works well. As we know that a train has several components to check. It is very difficult to identify whether a train is in a good condition or needs repairing. The purpose of this work is to proposes a model of operational feasibility by several main criteria: bogie, breaking system, boffer, electric coupler, and safety kit. In its experiment phase, this model uses Tsukamoto Fuzzy Inference System to decide that a train is in good condition or need repairing. In evaluation phase we compare this model with traditional method and this model shows exactly 99% of the same result. It is suggested for further work to include several methods such as Tsugeno and Mamdani fuzzy inference system.
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Wong, Shen Yuong, Keem Siah Yap, and Xiaochao Li. "A Genetic Algorithm Based Fuzzy Inference System for Pattern Classification and Rule Extraction." International Journal of Engineering & Technology 7, no. 4.35 (2018): 361. http://dx.doi.org/10.14419/ijet.v7i4.35.22762.
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Setting fuzzy rules is one of the paramount techniques in the design of a fuzzy system. For a simple system, fuzzy if-then rules are usually derived from the human experts. However, in the event of having multiple variables coupled with a few features, the classification problem will be getting more sophisticated, as a result human expert may not be able to derive proper rules. This paper presents a genetic-algorithm-based fuzzy inference system for extracting highly comprehensible fuzzy rules to be implemented in human practices without detailed computation (hereafter denoted as GA-FIS). The impetus for developing a new and efficient GA-FIS model arises from the need of constructing fuzzy rules directly from raw data sets that combines good approximation and classification properties with compactness and transparency. Therefore, our proposed GA-FIS method will first define the membership functions with logical interpretation which is amendable by domain experts to human understanding, and then genetic algorithm serves as an optimization tool to construct the best combination of rules in fuzzy inference system that can achieve higher classification accuracy and gain better interpretability. The proposed approach is applied to various benchmark and real world problems and the results show its validity.
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NGUYEN, THANH THUY, TOAN THANG NGUYEN, BINH CUONG THAC, and DINH KHANG TRAN. "A FUZZY DISTRIBUTED EXPERT SYSTEM DESIGN TOOL EXGEN AND ITS APPLICATION TO SENSORY FOODSTUFF EVALUATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 04 (1999): 383–88. http://dx.doi.org/10.1142/s0218488599000337.
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Since the appearance of MYCIN, expert systems have been widely and successfully developed for various scientific and technological researches and applications. These applications require more and more fuzzy information resources because of the uncertainty, inexactness in labeling facts using linguistic terms and expressing human expertise. Sensory foodstuff evaluation is among this kind of fuzzy expert system applications. In the frame of the research project on fuzzy expert systems for science and technology at the Hanoi University of Technology, we have developed an expert system building tool called EXGEN which has the following features: – Knowledge editing in the form of production rules using Vietnamese in the natural language-like syntax. The tool is also capable to verify the consistency of an acquired knowledge base. – Inference engine consisting of two principal inference mechanisms (forward and backward inference) and control strategy module. We proposed also some heuristics for choosing a potential inference trace, allowing to get more information about conclusions. – Possibility of establishing a configuration for a distributed working session. It would be possible to carry out: + a deduction over a shared rule base (RB) in the server, based on information acquired from workstations (common RB and conclusion, distributed fact base (FB)) + a deduction over a shared RB in the server with different cognitive tasks (including hypotheses fact and conclusions) on workstations (common RB and distributed FB) + deductions on workstations with distributed knowledge bases (Distributed RB and FB) We have already implemented an application expert system SENEXSYS for sensory foodstuff evaluation using the building tool EXGEN. Experimental results have shown that qualification given by the expert system is comparable to evaluation results obtained by following up Vietnamese standard TCVN 3215.79
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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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Ahmad, Gulzar, Muhammad Adnan Khan, Sagheer Abbas, Atifa Athar, Bilal Shoaib Khan, and Muhammad Shoukat Aslam. "Automated Diagnosis of Hepatitis B Using Multilayer Mamdani Fuzzy Inference System." Journal of Healthcare Engineering 2019 (February 5, 2019): 1–11. http://dx.doi.org/10.1155/2019/6361318.
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In this research, a new multilayered mamdani fuzzy inference system (Ml-MFIS) is proposed to diagnose hepatitis B. The proposed automated diagnosis of hepatitis B using multilayer mamdani fuzzy inference system (ADHB-ML-MFIS) expert system can classify the different stages of hepatitis B such as no hepatitis, acute HBV, or chronic HBV. The expert system has two input variables at layer I and seven input variables at layer II. At layer I, input variables are ALT and AST that detect the output condition of the liver to be normal or to have hepatitis or infection and/or other problems. The further input variables at layer II are HBsAg, anti-HBsAg, anti-HBcAg, anti-HBcAg-IgM, HBeAg, anti-HBeAg, and HBV-DNA that determine the output condition of hepatitis such as no hepatitis, acute hepatitis, or chronic hepatitis and other reasons that arise due to enzyme vaccination or due to previous hepatitis infection. This paper presents an analysis of the results accurately using the proposed ADHB-ML-MFIS expert system to model the complex hepatitis B processes with the medical expert opinion that is collected from the Pathology Department of Shalamar Hospital, Lahore, Pakistan. The overall accuracy of the proposed ADHB-ML-MFIS expert system is 92.2%.
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Siti, Kania Kushadiani, Buono Agus, and Nugroho Budi. "Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house." Computer Science and Information Technologies 3, no. 2 (2022): 116–25. https://doi.org/10.11591/csit.v3i2.pp116-125.
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As a scientific publishing house, Indonesian Institute of Sciences (LIPI) Press' encountered some problems in publication planning, mainly predicting the optimum production of publications. This study aimed to enhance a fuzzy inference system (FIS) parameters using the genetic algorithm (GA). The enhancements led to optimally predict the number of LIPI Press publications for the following year. The predictors used were the number of work units, the number of workers, and the publishing process duration. The dataset covered a five years range of total production of LIPI Press. Firstly, an expert set up the parameters of the fuzzy inference system denoted as a FIS expert. Next, we performed a FIS GA by applying the genetic algorithm and K-fold validation in splitting the training data and testing data. The FIS GA revealed optimum prediction with parameters that were composed of both population size (30), the probability of crossover (0.75), the probability of mutation (0.01), and the number of generations (150). The experiment results show that our enhanced FIS GA outperformed FIS expert approach.
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Mollo Neto, Mario, Irenilza de A. Nääs, Victor C. de Carvalho, and Antonio H. Q. Conceição. "Preventive diagnosis of dairy cow lameness." Engenharia Agrícola 34, no. 3 (2014): 577–89. http://dx.doi.org/10.1590/s0100-69162014000300020.
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This research aimed to develop a Fuzzy inference based on expert system to help preventing lameness in dairy cattle. Hoof length, nutritional parameters and floor material properties (roughness) were used to build the Fuzzy inference system. The expert system architecture was defined using Unified Modelling Language (UML). Data were collected in a commercial dairy herd using two different subgroups (H1 and H2), in order to validate the Fuzzy inference functions. The numbers of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) responses were used to build the classifier system up, after an established gold standard comparison. A Lesion Incidence Possibility (LIP) developed function indicates the chances of a cow becoming lame. The obtained lameness percentage in H1 and H2 was 8.40% and 1.77%, respectively. The system estimated a Lesion Incidence Possibility (LIP) of 5.00% and 2.00% in H1 and H2, respectively. The system simulation presented 3.40% difference from real cattle lameness data for H1, while for H2, it was 0.23%; indicating the system efficiency in decision-making.
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Harkouss, Youssef. "Accurate modeling and optimization of microwave circuits and devices using adaptive neuro-fuzzy inference system." International Journal of Microwave and Wireless Technologies 3, no. 6 (2011): 637–45. http://dx.doi.org/10.1017/s1759078711000651.
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In this paper, an accurate neuro-fuzzy-based model is proposed for efficient computer-aided design (CAD) modeling and optimization of microwave circuits and devices. The adaptive neuro-fuzzy inference system (ANFIS) approach is used to determine the scattering parameters of a microstrip filter and is applied to the optimization design of this microstrip filter. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of artificial neural networks. The neuro-fuzzy model has been trained and tested with different sets of input/output data. Finally, different results, which confirm the validity of the proposed model, are reported.
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Wu, Da Qin, and Hui Yan. "Structure Model of CDIO Ability Evaluation Expert System Based on Fuzzy Petri Net." Applied Mechanics and Materials 263-266 (December 2012): 2164–67. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2164.
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CDIO is a kind of innovation mode of international engineering education, and CDIO capability evaluation is the main content implemented by this mode. In this thesis, the model of CDIO capability evaluation expert system is designed, and Fuzzy Petri Net technique is applied to conduct knowledge presentation and inference of inference engine. Besides, the construction of knowledge database and the algorithm and realization of inference mechanism are elaborated. CDIO capability level of the students can be evaluated intelligently by applying evaluation expert system, which can also feed back the existing key questions. In this way, the development and evaluation of CDIO capability are constituted into a loop, which improves the quality of talent training effectively.
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Zhang, Dunli. "Intelligent Diagnostic System for Starter Motor Testers Based on Fuzzy Neural Networks." Advances in Engineering Technology Research 12, no. 1 (2024): 99. https://doi.org/10.56028/aetr.12.1.99.2024.
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In response to the multiple and ambiguous faults characteristic of starter motor testers, a fault diagnosis expert system based on fuzzy neural networks has been proposed. This design leverages the strengths of neural networks, fuzzy control, and expert systems. Neural networks diagnose faults in testing equipment, while fuzzy membership functions represent the degree of these faults. The system offers fast inference speed and strong fault tolerance. A control program was developed using Matlab, featuring a new user-friendly human-machine interface to enhance usability and operational ease of the system.
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AHMADOV, Heybatulla, Elshan MANAFOV, Huseyngulu GULIYEV, and Farid HUSEYNOV. "A FUZZY LOGIC-BASED MULTI-SENSOR DIAGNOSTIC SYSTEM FOR TRACTION MOTOR BEARINGS IN RAILWAY APPLICATIONS." Transport Problems 20, no. 2 (2025): 73–84. https://doi.org/10.20858/tp.2025.20.2.06.
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This article focuses on the diagnosis of the bearings of the traction motors of electric railway and subway trains. One of the main sources of mechanical failures in a traction motor is its bearings. The failure of traction motor bearings, the factors that cause these failures, and the diagnostic methods for detecting them are investigated. At this time, faults in traction motor bearing monitoring systems are determined only by temperature. In this work, it is proposed to use a system with temperature, vibration, and noise to determine the technical condition of bearings. Such a multi-parameter system, unlike traditional ones, will help determine specific defects at an early stage. The expert system’s model, based on fuzzy logic and diagnostic parameters, can accurately predict the likelihood of bearing faults in real-time under changing operating conditions. A fuzzy expert system represents knowledge in the form of fuzzy productions and linguistic variables. The expert system model was developed using the Mamdani fuzzy inference algorithm of the Fuzzy Logic Toolbox package in the MATLAB computing environment. The application of fuzzy logic in generating a knowledge base and inference processes enables the formalization of a process for evaluating technical conditions based on incomplete, faulty, and potentially erroneous information and for making decisions about fault identification.
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Choi, Sang-Kyoon, and Jae-Saeng Kim. "Fuzzy Inference Engine for Ontology-based Expert Systems." Journal of the Korea Contents Association 9, no. 6 (2009): 45–52. http://dx.doi.org/10.5392/jkca.2009.9.6.045.
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Yershov, S. V., and R. М. Ponomarenko. "Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems." PROBLEMS IN PROGRAMMING, no. 2-3 (June 2016): 141–49. http://dx.doi.org/10.15407/pp2016.02-03.141.
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Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.
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Hu, Cheng-Kai, Fung-Bao Liu, and Cheng-Feng Hu. "A Set Covering-Based Diagnostic Expert System to Economic and Financial Applications." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 01 (2016): 91–107. http://dx.doi.org/10.1142/s0218488516500057.
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This paper considers the identification of problems which generate anomalies at firms through the observed symptoms on the basis of fuzzy relations and Zadeh's compositional rule of inference. A procedure for determining the fuzzy cause vector of an economic and financial diagnosis problem is proposed, which consists of the design of fuzzy relational matrix and the resolution of a system of fuzzy relational equations. An efficient algorithm for solving fuzzy relational equations in terms of the associated set covering problem is introduced. It utilizes a back-tracking method to generate each minimal covering, where no duplicate or non-minimal coverings exist. A numerical example of firms' insolvency causes diagnosis is also included.
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Mahanta, Juthika, and Subhasis Panda. "Fuzzy Expert System for Prediction of Prostate Cancer." New Mathematics and Natural Computation 16, no. 01 (2020): 163–76. http://dx.doi.org/10.1142/s1793005720500106.
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A fuzzy expert system (FES) for the prediction of prostate cancer (PC) is prescribed in this paper. Age, prostate-specific antigen (PSA), prostate volume (PV) and [Formula: see text] Free PSA ([Formula: see text]FPSA) are fed as inputs into the FES and prostate cancer risk (PCR) is obtained as the output. Using knowledge-based rules in Mamdani type inference method the output is calculated. If PCR [Formula: see text], then the patient shall be advised to go for a biopsy test for confirmation. The efficacy of the designed FES is tested against a clinical dataset. The true prediction for all the patients turns out to be [Formula: see text] whereas only for positive biopsy cases it rises to [Formula: see text]. This simple yet effective FES can be used as supportive tool for decision-making in medical diagnosis.
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Hwang, Cheng-Neng. "The Integrated Design of Fuzzy Collision-Avoidance and H∞-Autopilots on Ships." Journal of Navigation 55, no. 1 (2002): 117–36. http://dx.doi.org/10.1017/s0373463301001631.
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Collision avoidance remains the most important concern for ships at sea. Despite the electronic equipment now fitted on ships to support the mariner, expert experience is still essential when a ship is in danger of colliding with the others. To include these experts' experiences to resolve the problems of collision, we have designed a fuzzy collision-avoidance expert system that includes a knowledge base to store facts and rules, an inference engine to simulate experts' decisions and a fuzzy interface device. Either a quartermaster or an autopilot system can then implement the avoidance action proposed in the research. To perform the task of collision-avoidance effectively, a robust autopilot system using the state space H∞ control methodology has been designed to steer a ship safely for various conditions at sea in performing course keeping, course-changing and route-tracking more robustly. The integration of fuzzy collision-avoidance and H∞autopilot systems is then proposed in this paper.
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Кривуля, Г. Ф., and В. І. Сергіенко. "Using intellectual means for diagnosis of wireless sensor network." ВІСНИК СХІДНОУКРАЇНСЬКОГО НАЦІОНАЛЬНОГО УНІВЕРСИТЕТУ імені Володимира Даля, no. 5(253) (September 5, 2019): 50–52. http://dx.doi.org/10.33216/1998-7927-2019-253-5-50-52.
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The paper discusses the adaptive neuro-fuzzy inference system ANFIS for intellectual diagnostics of large-scale wireless sensor networks. The solution for functional diagnostics of wireless sensor network is realized by the expert system designed on the knowledge base in the form of a neuron-fuzzy network.
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Tavana, Madjid, and Vahid Hajipour. "A practical review and taxonomy of fuzzy expert systems: methods and applications." Benchmarking: An International Journal 27, no. 1 (2019): 81–136. http://dx.doi.org/10.1108/bij-04-2019-0178.
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Purpose Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems. Design/methodology/approach The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems. Findings The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field. Originality/value Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.
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Geramian, Arash, Mohammad Reza Mehregan, Nima Garousi Mokhtarzadeh, and Mohammadreza Hemmati. "Fuzzy inference system application for failure analyzing in automobile industry." International Journal of Quality & Reliability Management 34, no. 9 (2017): 1493–507. http://dx.doi.org/10.1108/ijqrm-03-2016-0026.
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Purpose Nowadays, quality is one of the most important key success factors in the automobile industry. Improving the quality is based on optimizing the most important quality characteristics and usually launched by highly applied techniques such as failure mode and effect analysis (FMEA). According to the literature, however, traditional FMEA suffers from some limitations. Reviewing the literature, on one hand, shows that the fuzzy rule-base system, under the artificial intelligence category, is the most frequently applied method for solving the FMEA problems. On the other hand, the automobile industry, which highly takes advantages of traditional FMEA, has been deprived of benefits of fuzzy rule-based FMEA (fuzzy FMEA). Thus, the purpose of this paper is to apply fuzzy FMEA for quality improvement in the automobile industry. Design/methodology/approach Firstly, traditional FMEA has been implemented. Then by consulting with a six-member quality assurance team, fuzzy membership functions have been obtained for risk factors, i.e., occurrence (O), severity (S), and detection (D). The experts have also been consulted about constructing the fuzzy rule base. These evaluations have been performed to prioritize the most critical failure modes occurring during production of doors of a compact car, manufactured by a part-producing company in Iran. Findings Findings indicate that fuzzy FMEA not only solves problems of traditional FMEA, but also is highly in accordance with it, in terms of some priorities. According to results of fuzzy FMEA, failure modes E, pertaining to the sash of the rear right door, and H, related to the sash of the front the left door, have been ranked as the most and the least critical situations, respectively. The prioritized failures could be considered to facilitate future quality optimization. Practical implications This research provides quality engineers of the studied company with the chance of ranking their failure modes based on a fuzzy expert system. Originality/value This study utilizes the fuzzy logic approach to solve some major limitations of FMEA, an extensively applied method in the automobile industry.
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Adeyemi, H. O., S. B. Adejuyigbe, S. O. Ismaila, and A. F. Adekoya. "Low back pain assessment application for construction workers." Journal of Engineering, Design and Technology 13, no. 3 (2015): 419–34. http://dx.doi.org/10.1108/jedt-02-2013-0008.
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Purpose – The purpose of this paper is to develop an expert system capable of assessing risk associated with manual lifting in construction tasks and proffer some first aid advices which are comparable with those obtainable from human experts. Design/methodology/approach – The expert system, musculoskeletal disorders – risk evaluation expert system (MSDs-REES), used Microsoft.Net C# programming language to write the algorithm of the fuzzy inference system with variables load, posture and frequency of lift as inputs and risk of low back pain as the output. The algorithm of the inference engine applied sets of rules to generate the output variable in crisp value. Findings – The result of validation, between the human experts’ calculated risk values and MSDs-REES-predicted risk values, indicated a correlation coefficient of 0.87. Between the predicted risk values generated using MSDs-REES and the existing package (MATLAB version 7.8), there was a strong positive relationship statistically with correlation coefficient of 0.97. Originality/value – The study provided a very simple expert system which has the ability to provide some medical-related injury prevention advice and first aid information for injury management, giving it a unique attribute over the existing applications.