Relevant bibliographies by topics / Fuzzy logic inference system

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Academic literature on the topic 'Fuzzy logic inference system'

Author: Grafiati
Published: 4 June 2025
Last updated: 6 July 2025

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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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Dissertations / Theses on the topic "Fuzzy logic inference system"

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Rybalka, A. I., A. S. Kutsenko, and S. V. Kovalenko. "Modelling of an automated food quality assessment system based on fuzzy inference." Thesis, Харківський національний університет радіоелектроніки, 2020. http://openarchive.nure.ua/handle/document/14769.

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The purpose of this study is to create a methodology for developing an automated system for assessing the quality of food products based on a comprehensive quality indicator and the use of fuzzy logic theory, namely, fuzzy inference. In our opinion, such an approach to quality assessment can reduce the subjective component that has a significant impact on making a final decision. The system, built on a given algorithm, allows us to assess the quality of food products, taking into account the data of laboratory studies on measurable quality indicators and expert opinions on difficult to measure indicators.
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Funsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.

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Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning.
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García, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.

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Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.
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França, Daniel cruz de. "Modelagem de um adaptive neuro fuzzy inference system para análise de risco em projetos." Universidade Federal da Paraíba, 2016. http://tede.biblioteca.ufpb.br:8080/handle/tede/8163.

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Submitted by Maike Costa (maiksebas@gmail.com) on 2016-04-29T13:48:06Z No. of bitstreams: 1 arquivo total.pdf: 1906817 bytes, checksum: 6bf3c54782cfdea75b86311d9bc28cb9 (MD5)<br>Made available in DSpace on 2016-04-29T13:48:06Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 1906817 bytes, checksum: 6bf3c54782cfdea75b86311d9bc28cb9 (MD5) Previous issue date: 2016-02-22<br>Several researches highlight the importance of risk management in project management. Many authors propose traditional models with statistical and deterministic methods, though some risk project management issues are based on conceptual frameworks, expert opinion and human experience. This kind of problem makes difficult the use of classical models, but can be mathematically treated using fuzzy logic. In addition, historical data of projects can provide information about the organization's risk analysis experience and be modelled by a learning mechanism. The method used in this work is the Adaptive Neuro-fuzzy Inference System (ANFIS), which is capable of aggregating the mathematical treatment capacity of conceptual models with a hybrid learning algorithm. Thus, the aim of this study is to model an ANFIS that is able to analyze the risks of projects. A set of projects was analyzed by means of a risk management checklist with factors arranged in a risk breakdown structure (RBS). Estimates were made using probability and impact matrix, and expert opinion. The risk of each project was defined as an integer between 1 and 10. To select the best model among 32 different ANFIS settings, 84% of the data were used in 10-fold cross-validation. The model with the best results in validation process was selected and tested with the remaining data. The results attained in the evaluation were: mean squared error (MSE) of 0.2207, mean absolute error (MAE) of 0.3084, coefficient of determination (R²) of 0.9733 and 80% of accuracy. These results indicate that the project risk management can be successfully performed by ANFIS. This enables the modeling of knowledge and human experience and can reduce costs of skilled labor and improve the speed of analysis.<br>Diversas pesquisas ressaltam a importância do gerenciamento de risco na gestão de projetos. Muitos autores propõem modelos tradicionais com métodos estatísticos ou determinísticos, entretanto alguns problemas de gerenciamento de risco em projetos são baseados em estruturas conceituais, na opinião especializada e na experiência humana. Esse tipo de problema dificulta a utilização de modelos clássicos, mas pode ser tratado matematicamente por meio da lógica fuzzy. Além disso, dados históricos de projetos podem fornecer informações sobre a experiência de analise de risco da organização e ser modelados por mecanismo de aprendizagem. O mecanismo utilizado nesse trabalho é o Adaptive Neuro-fuzzy Inferece System (ANFIS), que é capaz de agregar a capacidade de tratamento matemático de modelos conceituais com um algoritmo de aprendizagem híbrido. Desse modo, o objetivo desse trabalho é modelar um Adaptive Neuro-fuzzy Inferece System capaz de analisar os riscos de projetos. Um conjunto de projetos foi analisado por meio de uma lista de verificação com fatores de risco organizados em uma estrutura analítica de risco (EAR). As estimativas foram realizadas por meio de matrizes de probabilidade e impacto e opinião especializada. O risco de cada projeto foi definido como um número inteiro entre 1 e 10. Foram utilizados 84% dados na validação cruzada 10-fold para seleção do melhor modelo entre 32 diferentes configurações de ANFIS. O modelo com os melhores resultados de validação foi selecionado e testado com os dados restantes. Os resultados alcançados na avaliação foram: erro quadrático médio (MSE) de 0,2207, erro absoluto médio de 0,3084, coeficiente de determinação (R²) de 0,9733 e acurácia de 80%. Esses resultados indicam que o gerenciamento de riscos em projetos pode ser realizado com sucesso através do ANFIS. Isso possibilita a modelagem de conhecimento e experiências humanas e pode diminuir custos com mão de obra especializada e aumentar a velocidade das análises.
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Mohd, Noor Mohd Junaizee. "Application of knowledge-based fuzzy inference system on high voltage transmission line maintenance." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/16050/1/Mohd_Junaizee_Mohd_Noor_Thesis.pdf.

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A majority of utilities conduct maintenance of transmission line components based on the results of routine visual inspection. The inspection is normally done by inspectors who detect defects by visually checking transmission line components either from the air (in helicopters), from the ground (by using high-powered binoculars) or from the top of the structure (by climbing the structure). The main problems with visual inspection of transmission lines are that the determination of the defects varies depending on the inspectors' knowledge and experience and that the defects are often reported qualitatively using vague and linguistic terms such as "medium crack", "heavy rust", "small deflection". As a result of these drawbacks, there is a large variance and inconsistency in defect reporting (which, in time, makes it difficult for the utility to monitor the condition of the components) leading to ineffective or wrong maintenance decisions. The use of inspection guides has not been able to fully address these uncertainties. This thesis reports on the application of a visual inspection methodology that is aimed at addressing the above-mentioned problems. A knowledge-based Fuzzy Inference System (FIS) is designed using Matlab's Fuzzy Logic Toolbox as part of the methodology and its application is demonstrated on utility visual inspection practice of porcelain cap and pin insulators. The FIS consists of expert-specified input membership functions (representing various insulator defect levels), output membership functions (indicating the overall conditions of the insulator) and IF-THEN rules. Consistency in the inspection results is achieved because the condition of the insulator is inferred using the same knowledge-base in the FIS rather than by individual inspectors. The output of the FIS is also used in a mathematical model that is developed to suggest appropriate component replacement date. It is hoped that the methodology that is introduced in this research will help utilities achieve better maintenance management of transmission line assets.
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Mohd, Noor Mohd Junaizee. "Application of knowledge-based fuzzy inference system on high voltage transmission line maintenance." Queensland University of Technology, 2004. http://eprints.qut.edu.au/16050/.

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A majority of utilities conduct maintenance of transmission line components based on the results of routine visual inspection. The inspection is normally done by inspectors who detect defects by visually checking transmission line components either from the air (in helicopters), from the ground (by using high-powered binoculars) or from the top of the structure (by climbing the structure). The main problems with visual inspection of transmission lines are that the determination of the defects varies depending on the inspectors' knowledge and experience and that the defects are often reported qualitatively using vague and linguistic terms such as "medium crack", "heavy rust", "small deflection". As a result of these drawbacks, there is a large variance and inconsistency in defect reporting (which, in time, makes it difficult for the utility to monitor the condition of the components) leading to ineffective or wrong maintenance decisions. The use of inspection guides has not been able to fully address these uncertainties. This thesis reports on the application of a visual inspection methodology that is aimed at addressing the above-mentioned problems. A knowledge-based Fuzzy Inference System (FIS) is designed using Matlab's Fuzzy Logic Toolbox as part of the methodology and its application is demonstrated on utility visual inspection practice of porcelain cap and pin insulators. The FIS consists of expert-specified input membership functions (representing various insulator defect levels), output membership functions (indicating the overall conditions of the insulator) and IF-THEN rules. Consistency in the inspection results is achieved because the condition of the insulator is inferred using the same knowledge-base in the FIS rather than by individual inspectors. The output of the FIS is also used in a mathematical model that is developed to suggest appropriate component replacement date. It is hoped that the methodology that is introduced in this research will help utilities achieve better maintenance management of transmission line assets.
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Havlíček, Petr. "Spornost fuzzy logických teorií v odvozovacích systémech." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-15839.

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This thesis focus on consistency of a specific class of fuzzy logic theories that represent certain inference system. This class of theories is defined as theories containing especially so called special axioms representing rules of modeled inference system and evaluated set of formulas representing case data. Functional approach is used to develop three popular fuzzy calculi: the Gödel logic, Łukasiewicz logic and product logic. As a language it is used the language of first order propositional fuzzy logic with valuation. To check consistency we use the concept of inconsistency degree and in Łukasiewicz logic also the principle of polar index. The concept of consistency degree is also described, but not used. Simple algorithm is developed to check consistency of theory upon the basis of inconsistency degree principle. A method of use of polar index is also described and illustrated. For each fuzzy theory a term of corresponding classical theory is defined. Then consistency of fuzzy theories and their corresponding classical theories are compared. The results of comparison are presented on the example of the ad-hoc created diagnostic inference system MEDSYS II. In the end the relation between consistency of fuzzy theory of inference system and it's corresponding theory is introduced for all three used calculi and both contradiction concepts.
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Pavlick, Bay. "A fuzzy logic based controller to provide end-to-end congestion control for streaming media applications." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001253.

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Shekarriz, Mona. "The foundation of capability modelling : a study of the impact and utilisation of human resources." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5257.

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This research aims at finding a foundation for assessment of capabilities and applying the concept in a human resource selection. The research identifies a common ground for assessing individuals’ applied capability in a given job based on literature review of various disciplines in engineering, human sciences and economics. A set of criteria is found to be common and appropriate to be used as the basis of this assessment. Applied Capability is then described in this research as the impact of the person in fulfilling job requirements and also their level of usage from their resources with regards to the identified criteria. In other words how their available resources (abilities, skills, value sets, personal attributes and previous performance records) can be used in completing a job. Translation of the person’s resources and task requirements using the proposed criteria is done through a novel algorithm and two prevalent statistical inference techniques (OLS regression and Fuzzy) are used to estimate quantitative levels of impact and utilisation. A survey on post graduate students is conducted to estimate their applied capabilities in a given job. Moreover, expert academics are surveyed on their views on key applied capability assessment criteria, and how different levels of match between job requirement and person’s resources in those criteria might affect the impact levels. The results from both surveys were mathematically modelled and the predictive ability of the conceptual and mathematical developments were compared and further contrasted with the observed data. The models were tested for robustness using experimental data and the results for both estimation methods in both surveys are close to one another with the regression models being closer to observations. It is believed that this research has provided sound conceptual and mathematical platforms which can satisfactorily predict individuals’ applied capability in a given job. This research has contributed to the current knowledge and practice by a) providing a comparison of capability definitions and uses in different disciplines, b) defining criteria for applied capability assessment, c) developing an algorithm to capture applied capabilities, d) quantification of an existing parallel model and finally e) estimating impact and utilisation indices using mathematical methods.
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Aslan, Muhittin. "Modeling The Water Quality Of Lake Eymir Using Artificial Neural Networks (ann) And Adaptive Neuro Fuzzy Inference System (anfis)." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610211/index.pdf.

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Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate, Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: to develop models with ANN to predict DO concentration in Lake Eymir with high fidelity to actual DO data, to compare the success (prediction capacity) of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. For modeling studies &ldquo<br>Matlab R 2007b&rdquo<br>software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS Graphical User Interface. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.
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Books on the topic "Fuzzy logic inference system"

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United States. National Aeronautics and Space Administration., ed. Learning fuzzy logic control system. National Aeronautics and Space Administration, 1994.

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Melin, Patricia, Oscar Castillo, Janusz Kacprzyk, Marek Reformat, and William Melek, eds. Fuzzy Logic in Intelligent System Design. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67137-6.

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Dongbo, Liu, ed. A fuzzy PROLOG database system. Research Studies Press, 1990.

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Maria, Bojadziev, ed. Fuzzy logic for business, finance, and management. 2nd ed. World Scientific, 2007.

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Maria, Bojadziev, ed. Fuzzy logic for business, finance, and management. World Scientific, 1997.

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Ahmed, Jameel, Mohammed Yakoob Siyal, Shaheryar Najam, and Zohaib Najam. Fuzzy Logic Based Power-Efficient Real-Time Multi-Core System. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3120-5.

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Castillo, Oscar, and Patricia Melin, eds. Fuzzy Logic and Neural Networks for Hybrid Intelligent System Design. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22042-5.

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Jamshidi, Mohammad. Large-scale systems: Modeling, control, and fuzzy logic. Prentice Hall, 1997.

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Karr, C. L. An adaptive system for process control. U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Karr, C. L. An adaptive system for process control. U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Book chapters on the topic "Fuzzy logic inference system"

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Chapman, William, and Arjab Singh Khuman. "Water Carbonation Fuzzy Inference System." In Fuzzy Logic. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66474-9_15.

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Tan, Joey Sing Yee, and Amandeep S. Sidhu. "Fuzzy Inference System." In Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15585-8_4.

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Chen, Guoqing. "A FFD Inference System." In Fuzzy Logic in Data Modeling. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4068-7_8.

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Maia, Daniel Helder, and Arjab Singh Khuman. "A Mamdani Fuzzy Logic Inference System to Estimate Project Cost." In Fuzzy Logic. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66474-9_10.

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Hughes, Sophie, and Arjab Singh Khuman. "Automatic Camera Flash Using a Mamdani Type One Fuzzy Inference System." In Fuzzy Logic. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66474-9_13.

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Arita, Seizaburo, Masaya Yoneda, and Yoshimi Hori. "Supporting System for the Diagnosis of Diabetes Mellitus Based on Glucose Tolerance Test Responses Using a Fuzzy Inference." In Fuzzy Logic. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_28.

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Li, Fangyi, and Qiang Shen. "Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Inference Systems." In Fuzzy Rule-Based Inference. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0491-0_1.

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Wienholt, Willfried. "Improving a Fuzzy Inference System by Means of Evolution Strategy." In Fuzzy Logik. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-79386-8_23.

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Almutairi, Khleef, Samuel Morillas, Pedro Latorre-Carmona, and Makan Dansoko. "A Fuzzy Logic Inference System for Display Characterization." In Pattern Recognition and Image Analysis. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36616-1_5.

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Katoch, Rachita, and Rosepreet Kaur Bhogal. "Edge Detection Using Fuzzy Logic (Fuzzy Sobel, Fuzzy Template, and Fuzzy Inference System)." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5903-2_76.

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Conference papers on the topic "Fuzzy logic inference system"

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Körösi, Ladislav, Jana Paulusová, and Oliver Halaš. "Adaptive Neuro Fuzzy Inference System for Programmable Logic Controller." In 2025 Cybernetics & Informatics (K&I). IEEE, 2025. https://doi.org/10.1109/ki64036.2025.10916472.

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Amin, Ahmad Faishol, Ronny Cahyadi Utomo, and Khoirul Azis Rifa’i. "Comparing Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for Auto-Cooling System in Generator Rotor Straightening." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10747980.

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Srichiangsa, Theeraphong, Piyapath Siratarnsophon, Sirichai Wattanasophon, and Sarinee Ouitrakul. "Comparative Analysis of PID, Self-Tunning PID, and Adaptive Neuro-Fuzzy Logic Inference System Controllers for BLDC Motor Speed Control." In 2024 27th International Conference on Electrical Machines and Systems (ICEMS). IEEE, 2024. https://doi.org/10.23919/icems60997.2024.10921276.

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Tayal, Shikha, Taskeen Zaidi, and Preeti Gera. "Utilization of Support Vector Machines (SVM), Fuzzy Logic (FL) & Adaptive Neuro-Fuzzy Inference System (ANFIS) for Carrying Out Proficient Energy Routing in 5G Wireless Networks." In 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI). IEEE, 2024. https://doi.org/10.1109/icscai61790.2024.10866528.

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Jayaram, Balasubramaniam, Kakarla Narayana, and V. Vetrivel. "Fuzzy Inference System based Contrast Enhancement." In 7th conference of the European Society for Fuzzy Logic and Technology. Atlantis Press, 2011. http://dx.doi.org/10.2991/eusflat.2011.13.

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Keller, James M., and Ronald R. Yager. "Fuzzy Logic Inference Neural Networks." In 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, edited by David P. Casasent. SPIE, 1990. http://dx.doi.org/10.1117/12.969771.

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Cingolani, Pablo, and Jesus Alcala-Fdez. "jFuzzyLogic: a robust and flexible Fuzzy-Logic inference system language implementation." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251215.

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Dhami, S. S., S. S. Bhasin, and P. B. Mahapatra. "Design of a Fuzzy Logic Controller Using ANFIS for Accurate Position Control of a Pneumatic Servo System." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66940.

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A methodology for designing a Sugeno type Fuzzy Logic Controller (FLC) for accurate position control of a pneumatic servo system is presented. Adaptive Neuro Fuzzy Inference System technique is employed to construct a fuzzy inference system whose membership function parameters are tuned using a training data set comprising of input/output signal of the pneumatic servo system with proportional control. Hybrid backpropogation-least square algorithm is used for training of the Fuzzy Inference System (FIS). The resulting FIS optimally projected the behavior of training data set. To obtain the desired steady-state response, the fuzzy inference system is further tuned using the expert knowledge of the input/output response of the system. The system response for various reference inputs is compared quantitatively with that of the system without fuzzy logic controller, and excellent improvement in steady-state response is observed.
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Itoh, Hideo, Masanobu Watanabe, Seiji Mukai, and Hiroyoshi Yajima. "Optoelectronic fuzzy logic inference system using beam scanning laser diodes." In Optical Computing. Optica Publishing Group, 1993. http://dx.doi.org/10.1364/optcomp.1993.owd.5.

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Kalandyk, Dawid, Bogdan Kwiatkowski, and Damian Mazur. "Application of Mamdani Fuzzy Logic Inference System to Optimise CNC Machine Motion Dynamics." In 2023 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE, 2023. http://dx.doi.org/10.1109/fuzz52849.2023.10309802.

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Reports on the topic "Fuzzy logic inference system"

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Reveiz-Herault, Alejandro, and Carlos Eduardo León-Rincón. Operational risk management using a fuzzy logic inference system. Banco de la República, 2009. http://dx.doi.org/10.32468/be.574.

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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.
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Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER, and Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.

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In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of inference rules that will make it possible. On the basis of a diagnosis such as the Energy Performance Certificate, it will help establishing a list of priority actions. The dynamics that drive this project permit to decrease the subjectivity of a human decisions making scheme. While simulation generates digital technical data, interpretation requires the translation of this data into natural language. The purpose is to automate the translation of the results to provide advice and facilitate decision-making. In medicine, the diagnostic phase is a process by which a disease is identified by its symptoms. Similarly, the idea of the process is to target the faulty elements potentially responsible for poor performance and to propose remedial solutions. The system is based on the development of fuzzy logic rules [2],[3]. This choice was made to be able to manipulate notions of membership with truth levels between 0 and 1, and to deliver messages in a linguistic form, understandable by non-specialist users. For example, if performance is low and parameter x is unfavourable, the algorithm can gives an incentive to improve the parameter such as: "you COULD, SHOULD or MUST change parameter x". Regarding energy performance analysis, the following domains are addressed: heating, domestic hot water, cooling, lighting. Regarding the parameters, the analysis covers the following topics: Characteristics of the building envelope. and of the technical installations (heat production-distribution, ventilation system, electric lighting, etc.). This paper describes the methodology used, lists the fields studied and outlines the expected outcomes of the project.
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House, William. Fuzzy Logic as a Tool to Compare Reliability of Torsion Bar System. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada513266.

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Pan, Juiyao, Guilherme N. DeSouza, and Avinash C. Kak. FuzzyShell: A Large-Scale Expert System Shell Using Fuzzy Logic for Uncertainty Reasoning. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada335107.

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Chernichovsky, Dov. A Fuzzy Logic Approach Toward Solving the Analytic Maze of Health System Financing. National Bureau of Economic Research, 2001. http://dx.doi.org/10.3386/w8470.

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Bennaoui, Ahmed, AISSA AMEUR, and SLAMI SAADI. Moth-Flame Optimizer Algorithm For Optimal Of Fuzzy Logic Controller for nonlinear system. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2304p4802037.

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Zyikin, P. V. Adjusting the roughness of the part surface by means of a fuzzy logic-based longitudinal feed control system. Ailamazyan Program Systems Institute of Russian Academy of Sciences, 2024. http://dx.doi.org/10.12731/ofernio.2023.25270.

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Zyikin, P. V. Adjusting the roughness of the part surface by means of a fuzzy logic-based longitudinal feed control system. Ailamazyan Program Systems Institute of Russian Academy of Sciences, 2024. http://dx.doi.org/10.12731/ofernio.2024.25284.

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Baader, Franz, and Barbara Morawska. Matching with respect to general concept inclusions in the Description Logic EL. Technische Universität Dresden, 2014. http://dx.doi.org/10.25368/2022.205.

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Matching concept descriptions against concept patterns was introduced as a new inference task in Description Logics (DLs) almost 20 years ago, motivated by applications in the Classic system. For the DL EL, it was shown in 2000 that the matching problem is NP-complete. It then took almost 10 years before this NP-completeness result could be extended from matching to unification in EL. The next big challenge was then to further extend these results from matching and unification without a TBox to matching and unification w.r.t. a general TBox, i.e., a finite set of general concept inclusions. For unification, we could show some partial results for general TBoxes that satisfy a certain restriction on cyclic dependencies between concepts, but the general case is still open. For matching, we solve the general case in this paper: we show that matching in EL w.r.t. general TBoxes is NP-complete by introducing a goal-oriented matching algorithm that uses non-deterministic rules to transform a given matching problem into a solved form by a polynomial number of rule applications. We also investigate some tractable variants of the matching problem.
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