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Academic literature on the topic 'Neuro-Fuzzy Approach'

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Published: 20 April 2024

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Journal articles on the topic "Neuro-Fuzzy Approach"

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Simiński, Krzysztof. "Neuro-rough-fuzzy approach for regression modelling from missing data." International Journal of Applied Mathematics and Computer Science 22, no. 2 (June 1, 2012): 461–76. http://dx.doi.org/10.2478/v10006-012-0035-4.

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Neuro-rough-fuzzy approach for regression modelling from missing dataReal life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.
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Han, Man-Wook, and Peter Kopacek. "Neuro-Fuzzy Approach in Service Robotics." IFAC Proceedings Volumes 29, no. 1 (June 1996): 760–65. http://dx.doi.org/10.1016/s1474-6670(17)57753-8.

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Ray, Kumar S., and Jayati Ghoshal. "Neuro Fuzzy Approach to Pattern Recognition." Neural Networks 10, no. 1 (January 1997): 161–82. http://dx.doi.org/10.1016/s0893-6080(96)00056-1.

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Biswas, Saroj, Monali Bordoloi, and Biswajit Purkayastha. "Review on Feature Selection and Classification using Neuro-Fuzzy Approaches." International Journal of Applied Evolutionary Computation 7, no. 4 (October 2016): 28–44. http://dx.doi.org/10.4018/ijaec.2016100102.

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This research article attempts to provide a recent survey on neuro-fuzzy approaches for feature selection and classification. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and for providing some insight to the user about the symbolic knowledge embedded within the network. The neuro–fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective of this article is to provide a generic introduction and a recent survey to neuro-fuzzy approaches for feature selection and classification in a wide area of machine learning problems. Some of the existing neuro-fuzzy models are also applied on standard datasets to demonstrate the applicability of neuro-fuzzy approaches.
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Rutkowska, Danuta, and Yoichi Hayashi. "Neuro-Fuzzy Systems Approaches." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (June 20, 1999): 177–85. http://dx.doi.org/10.20965/jaciii.1999.p0177.

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Two major approaches to neuro-fuzzy systems are distinguished in the paper. The previous one refers to fuzzy neural networks, which are neural networks with fuzzy signals, and/or fuzzy weights, as well as fuzzy transfer functions. The latter approach concerns neuro-fuzzy systems in the form of multilayer feed-forward networks, which differ from standard neural networks, because elements of particular layers conduct different operations than standard neurons. These structures are neural network representations of fuzzy systems and they are also called connectionist models of fuzzy systems, adaptive fuzzy systems, fuzzy inference neural networks, etc. Two different defuzzifiers, applied to fuzzy systems, are in focus of the paper. Center-of-sums method is an example of parametric defuzzification. Standard neural networks a defuzzifier presents nonparametric approach to defuzzification. For both cases learning algorithms of neuro-fuzzy systems are proposed. These algorithms take a form of recursions derived based on the momentum back-propagation method. Computer simulation demonstrates a comparison between performance of neuro-fuzzy systems with the parametric and nonparametric defuzzifier. Truck backer-upper control problem has been used to illustrate the systems performance. Conclusions concerning the simulation results are summarized. The paper pertains many references on neuro-fuzzy systems, especially selected publications of Czogala, whom it is dedicated.
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Amirkhani, Abdollah, Hosna Nasiriyan-Rad, and Elpiniki I. Papageorgiou. "A Novel Fuzzy Inference Approach: Neuro-fuzzy Cognitive Map." International Journal of Fuzzy Systems 22, no. 3 (December 23, 2019): 859–72. http://dx.doi.org/10.1007/s40815-019-00762-3.

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Nowicki, Robert. "On classification with missing data using rough-neuro-fuzzy systems." International Journal of Applied Mathematics and Computer Science 20, no. 1 (March 1, 2010): 55–67. http://dx.doi.org/10.2478/v10006-010-0004-8.

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On classification with missing data using rough-neuro-fuzzy systemsThe paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.
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Sadeghi-Niaraki, Abolghasem, Ozgur Kisi, and Soo-Mi Choi. "Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods." PeerJ 8 (August 14, 2020): e8882. http://dx.doi.org/10.7717/peerj.8882.

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This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970–2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it.
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Srinivasan, Santhoshkumar, and Dhinesh Babu L.D. "A Neuro-Fuzzy Approach to Detect Rumors in Online Social Networks." International Journal of Web Services Research 17, no. 1 (January 2020): 64–82. http://dx.doi.org/10.4018/ijwsr.2020010104.

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Along with true information, rumors spread in online social networks (OSN) on an unprecedented scale. In recent days, rumor identification gains more interest among the researchers. Finding rumors also poses other critical challenges like noisy and imprecise input data, data sparsity, and unclear interpretations of the output. To address these issues, we propose a neuro-fuzzy classification approach called the neuro-fuzzy rumor detector (NFRD) to automatically identify the rumors in OSNs. NFRD quickly transforms the input to fuzzy rules which classify the rumor. Neural networks handle larger input data. Fuzzy systems are better in handling uncertainty and imprecision in input data by producing fuzzy rules that effectively eliminate the unclear inputs. NFRD also considers the semantic aspects of information to ensure better classification. The neuro-fuzzy approach addresses the most common problems such as uncertainty elimination, noise reduction, and quicker generalization. Experimental results show the proposed approach performs well against state-of-the-art rumor detecting techniques.
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VAIRAPPAN, CATHERINE, SHANGCE GAO, ZHENG TANG, and HIROKI TAMURA. "ANNEALED CHAOTIC LEARNING FOR TIME SERIES PREDICTION IN IMPROVED NEURO-FUZZY NETWORK WITH FEEDBACKS." International Journal of Computational Intelligence and Applications 08, no. 04 (December 2009): 429–44. http://dx.doi.org/10.1142/s1469026809002680.

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A new version of neuro-fuzzy system of feedbacks with chaotic dynamics is proposed in this work. Unlike the conventional neuro-fuzzy, improved neuro-fuzzy system with feedbacks is better able to handle temporal data series. By introducing chaotic dynamics into the feedback neuro-fuzzy system, the system has richer and more flexible dynamics to search for near-optimal solutions. In the experimental results, performance and effectiveness of the presented approach are evaluated by using benchmark data series. Comparison with other existing methods shows the proposed method for the neuro-fuzzy feedback is able to predict the time series accurately.
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Dissertations / Theses on the topic "Neuro-Fuzzy Approach"

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Sisman, Yilmaz Nuran Arzu. "A Temporal Neuro-fuzzy Approach For Time Series Analysis." Phd thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/570366/index.pdf.

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The subject of this thesis is to develop a temporal neuro-fuzzy system for fore- casting the future behavior of a multivariate time series data. The system has two components combined by means of a system interface. First, a rule extraction method is designed which is named Fuzzy MAR (Multivari- ate Auto-regression). The method produces the temporal relationships between each of the variables and past values of all variables in the multivariate time series system in the form of fuzzy rules. These rules may constitute the rule-base in a fuzzy expert system. Second, a temporal neuro-fuzzy system which is named ANFIS unfolded in - time is designed in order to make the use of fuzzy rules, to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data. The rule base of ANFIS unfolded in time contains temporal TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, Back-propagation learning algorithm is used. The system takes the multivariate data and the num- ber of lags needed which are the output of Fuzzy MAR in order to describe a variable and predicts the future behavior. Computer simulations are performed by using synthetic and real multivariate data and a benchmark problem (Gas Furnace Data) used in comparing neuro- fuzzy systems. The tests are performed in order to show how the system efficiently model and forecast the multivariate temporal data. Experimental results show that the proposed model achieves online learning and prediction on temporal data. The results are compared by other neuro-fuzzy systems, specifically ANFIS.
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Osut, Demet. "A Behavior Based Robot Control System Using Neuro-fuzzy Approach." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/109765/index.pdf.

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In autonomous navigation of mobile robots the dynamic environment is a source of problems. Because it is not possible to model all the possible conditions, the key point in the robot control is to design a system that is adaptable to different conditions and robust in dynamic environments. This study presents a reactive control system for a Khepera robot with the ability to navigate in a dynamic environment for reaching goal objects. The main motivation of this research is to design a robot control, which is robust to sensor errors and sudden changes and adaptable to different environments and conditions. Behavior based approach is used with taking the advantage of fuzzy reasoning in design. Experiments are made on Webots, which is a simulation environment for Khepera robot.
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Arslan, Dilek. "A Control System Using Behavior Hierarchies And Neuro-fuzzy Approach." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605743/index.pdf.

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In agent based systems, especially in autonomous mobile robots, modelling the environment and its changes is a source of problems. It is not always possible to effectively model the uncertainity and the dynamic changes in complex, real-world domains. Control systems must be robust to changes and must be able to handle these uncertainties to overcome this problem. In this study, a reactive behaviour based agent control system is modelled and implemented. The control system is tested in a navigation task using an environment, which has randomly placed obstacles and a goal position to simulate an environment similar to an autonomous robot&rsquo
s indoor environment. Then the control system was extended to control an agent in a multi-agent environment. The main motivation of this study is to design a control system which is robust to errors and easy to modify. Behaviour based approach with the advantages of fuzzy reasoning systems is used in the system.
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OLIVEIRA, CARLOS ALEXANDRE DOS SANTOS. "STRATEGIC GROUPS: ARESOURCE-BASED VIEW AND NEURO-FUZZY SYSTEMS APPROACH." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5856@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Desde sua formulação, no início da década de setenta, o conceito de grupo estratégico é objeto de pesquisas teóricas e empíricas que buscam confirmar sua existência, sua contribuição à avaliação da performance e à formação das estratégias das empresas. Este trabalho soma-se a estas pesquisas, utilizando os conceitos da Visão Resource- Based e a aplicação de ferramentas de inteligência computacional, neste caso as redes neurais e os sistemas de inferência fuzzy, com o objetivo de contribuir para a discussão deste tema na superação de suas limitações e dos novos desafios que o aumento da complexidade das arenas competitivas trouxeram para as pesquisas do gerenciamento estratégico. A Visão Resource-Based fornece a base teórica para o desenvolvimento dos construtos: grau de inimitabilidade e grau de imobilidade, resultantes da exploração estratégica dos recursos da empresa. Estes construtos são propostos como dimensões de avaliação da semelhança estratégica entre as empresas de uma arena competitiva. A inteligência computacional fornece os meios de extração de informações subjetivas, e presentes em ambientes complexos, através da simulação do aprendizado, percepção, evolução e adaptação do raciocínio humano. O resultado é a proposição de um modelo de avaliação da existência de grupos estratégicos, utilizando os construtos Grau de Inimitabilidade e Grau de Imobilidade, e Sistemas Neuro-fuzzy. Este modelo é aplicado ao setor de supermercados como teste de validação do mesmo.
Since its has introduced, in the beginning of the decade of seventy, the concept of strategic groups is object of theoretical and empirical research that aims to confirm its existence, its contribution to performance evaluation and the formulation of the strategies of the firms. This text join these research, using the Resource-Based Views framework and soft computing, in this case neural networks and fuzzy inference systems, with aims at contributing for the discussion of this subject to overcome its limitations and the new challenges, resulting increasingly complexity and competitive environment, for the strategic management research. The Resource-Based View framework supplies the theoretical underpinnings to use the inimitability degree and immobility degree, resultants of the strategical exploration of the resources of the firms, as constructors to evaluate firm strategic similarity in a competitive environment. Soft computing is a tool to extract subjective data from complexity environments, simulating the ability for learning, perception, evolution and adaptation of human reasoning. The result of this research is the proposal of a model to identify strategic groups, applying the constructors Inimitability Degree and Immobility Degree, and Neuro-fuzzy Inference Systems. To validate the model, a test is performed to the supermarkets industry.
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Wang, Liren. "An approach to neuro-fuzzy feedback control in statistical process control." Thesis, University of South Wales, 2001. https://pure.southwales.ac.uk/en/studentthesis/an-approach-to-neurofuzzy-feedback-control-in-statistical-process-control(7d9c736f-e85d-4873-a6bb-9bcea107d371).html.

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It is a difficult challenge to develop a feedback control system for Statistical Process Control (SPC) because there is no effective method that can be used to calculate the accurate magnitude of feedback control actions in traditional SPC. Suitable feedback adjustments are generated from the experiences of process engineers. This drawback means that the SPC technique can not be directly applied in an automatic system. This thesis is concerned with Fuzzy Sets and Fuzzy Logic applied to the uncertainty of relationships between the SPC (early stage) alarms and SPC implementation. Based on a number of experiments of the frequency distribution for shifts of abnormal process averages and human subjective decision, a Fuzzy-SPC control system is developed to generate the magnitude of feedback control actions using fuzzy inference. A simulation study which is written in C++ is designed to implement a Fuzzy-SPC controller with satisfactory results. To further reduce the control errors, a NeuroFuzzy network is employed to build NNFuzzy- SPC system in MATLAB. The advantage of the leaning capability of Neural Networks is used to optimise the parameters of the Fuzzy- X and Fuzzy-J? controllers in order to obtain the ideal consequent membership functions to adapt to the randomness of various processes. Simulation results show that the NN-Fuzzy-SPC control system has high control accuracy and stable repeatability. To further improve the practicability of a NN-Fuzzy-SPC system, a combined forecaster with EWMA chart and digital filter is designed to reduce the NN-Fuzzy-SPC control delay. For the EWMA chart, the smoothing constant 0 is investigated by a number of experiments and optimised in the forecast process. The Finite Impulse Response (FIR) lowpass filter is designed to smooth the input data (signal) fluctuations in order to reduce the forecast errors. An improved NN-Fuzzy-SPC control system which shows high control accuracy and short control delay can be applied in both automatic control and online quality control.
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Kim, Sungshin. "A neuro-fuzzy approach to optimization and control of complex nonlinear processes." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14820.

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Mountrakis, Georgios. "Context-Specific Preference Learning of One Dimensional Quantitative Geospatial Attributes Using a Neuro-Fuzzy Approach." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/MountrakisGX2004.pdf.

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Taghizadeh, Vahed Amir. "Fan And Pitch Angle Selection For Efficient Mine Ventilation Using Analytical Hierachy Process And Neuro Fuzzy Approach." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614320/index.pdf.

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Ventilation is a critical task in underground mining operation. Lack of a good ventilation system causes accumulation of harmful gases, explosions, and even fatalities. A proper ventilation system provides adequate fresh air to miners for a safe and comfortable working environment. Fans, which provide air flow to different faces of a mine, have great impact in ventilation systems. Thus, selection of appropriate fans for a mine is the acute task. Unsuitable selection of a fan decreases safety and production rate, which increases capital and operational costs. Moreover, pitch angle of fans&rsquo
blades plays an important role in fan&rsquo
s efficiency. Therefore, selection of a fan and its pitch angle, which yields the maximum efficiency, is an emerging issue for an efficient mine ventilation. The main objective of this research study is to provide a decision making methodology for the selection of a main fan and its appropriate pitch angle for efficient mine ventilation. Nowadays, analytical hierarchy process as multi criteria decision making is used, and it yields outputs based on pairwise comparison. On the other hand, Fuzzy Logic as a soft computing method was combined with analytical hierarchy process and combined model did not yield appropriate results
because Fuzzy AHP increased uncertainty ratio in this study. However, fuzzy analytical hierarchy process might be inapplicable when it faces with vague and complex data set. Soft computing methods can be utilized for complicated situations. One of the soft computing methods is a Neuro-Fuzzy algorithm which is used in classification and DM issues. This study has two phases: i) selection of an appropriate fan using Analytical Hierarchy Process (AHP) and Fuzzy Analytical Hierarchy Process (Fuzzy AHP) and ii) selection of an appropriate pitch angle using Neuro-Fuzzy algorithm and Fuzzy AHP method. This study showed that AHP can be effectively utilized for main fan selection. It performs better than Fuzzy AHP because FAHP contains more expertise and makes problems more complex for evaluating. When FAHP and Neuro-Fuzzy is compared for pitch angle selection, both methodologies yielded the same results. Therefore, utilization of Neuro-Fuzzy in situation with complicated and vague data will be applicable.
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[Verfasser], Habtamu Gezahegn Tolossa, and Silke [Akademischer Betreuer] Wieprecht. "Sediment transport computation using a data-driven adaptive neuro-fuzzy modelling approach / Habtamu Gezahegn Tolossa. Betreuer: Silke Wieprecht." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2012. http://d-nb.info/1024692574/34.

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KOTHAMASU, RANGANATH. "INTELLIGENT CONDITION BASED MAINTENANCE - A SOFT COMPUTING APPROACH TO SYSTEM DIAGNOSIS AND PROGNOSIS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141339344.

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Books on the topic "Neuro-Fuzzy Approach"

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Fuzzy-neuro approach to agent applications: From the AI perspective to modern ontology. Berlin: Springer, 2006.

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Lee, R. S. T. Fuzzy-neuro approach to agent applications: From the AI perspective to modern ontology. Berlin, DE: Springer, 2006.

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Jang, Jyh-Shing Roger. Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Upper Saddle River, NJ: Prentice Hall, 1997.

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Lee, Raymond S. T. Fuzzy-Neuro Approach to Agent Applications. Springer, 2005.

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Fuzzy-Neuro Approach to Agent Applications. Berlin/Heidelberg: Springer-Verlag, 2006. http://dx.doi.org/10.1007/3-540-30984-5.

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Lee, Raymond S. T. Fuzzy-Neuro Approach to Agent Applications. Springer, 2008.

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Sun, Chuen-Tsai, Eiji Mizutani, and Jyh-Shing Roger Jang. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, 1996.

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Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, 1996.

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Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Pearson Education, Limited, 1996.

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Minimization of Climatic Vulnerabilities on Mini-hydro Power Plants: Fuzzy AHP, Fuzzy ANP Techniques and Neuro-Genetic Model Approach. Mrinmoy Majumder, 2016.

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Book chapters on the topic "Neuro-Fuzzy Approach"

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Lemma, Tamiru Alemu. "Model Identification Using Neuro-Fuzzy Approach." In A Hybrid Approach for Power Plant Fault Diagnostics, 37–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71871-2_3.

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Gianferrara, P., R. Poluzzi, and N. Serina. "A Neuro-Fuzzy Approach for Process Modelling." In Fuzzy Logik, 382–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-79386-8_47.

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Symeonaki, Maria, Aggeliki Kazani, and Catherine Michalopoulou. "A Neuro-Fuzzy Approach to Measuring Attitudes." In Demography and Health Issues, 169–81. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76002-5_15.

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Bansal, Ajay Kumar, and Swati Mathur. "CBIR Feature Extraction Using Neuro-Fuzzy Approach." In Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing, 535–41. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2638-3_60.

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Shiu, Simon C. K., X. Z. Wang, and Daniel S. Yeung. "Neuro-Fuzzy Approach for Maintaining Case Bases." In Soft Computing in Case Based Reasoning, 259–73. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0687-6_11.

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Tingane, Monali, Amol Bhagat, Priti Khodke, and Sadique Ali. "Neuro-Fuzzy Approach for Dynamic Content Generation." In Advances in Intelligent Systems and Computing, 497–508. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47952-1_39.

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Stathacopoulou, Regina, Maria Grigoriadou, George D. Magoulas, and Denis Mitropoulos. "A Neuro-fuzzy Approach in Student Modeling." In User Modeling 2003, 337–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44963-9_46.

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Amadin, Frank Iwebuke, and Moses Eromosele Bello. "A Neuro Fuzzy Approach for Predicting Delirium." In Advances in Intelligent Systems and Computing, 692–99. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01054-6_50.

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Rutkowska, Danuta. "Neuro-Fuzzy Architectures Based on the Mamdani Approach." In Neuro-Fuzzy Architectures and Hybrid Learning, 105–26. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1802-4_4.

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Rutkowska, Danuta. "Neuro-Fuzzy Architectures Based on the Logical Approach." In Neuro-Fuzzy Architectures and Hybrid Learning, 127–63. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1802-4_5.

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Conference papers on the topic "Neuro-Fuzzy Approach"

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Tandale, Sayali, Alka S. Barhatte, Rajesh Ghongade, and Manisha Dale. "Arrhythmia classification using neuro fuzzy approach." In 2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall). IEEE, 2017. http://dx.doi.org/10.1109/icaccaf.2017.8344712.

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Morozov, Sergey M. "Neuro-fuzzy Approach for Batteries Depassivation." In 2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2022. http://dx.doi.org/10.1109/elconrus54750.2022.9755528.

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Tyagi, Lakshya, and Abhishek Singhal. "Neuro-Fuzzy Approach to Explosion Consequence Analysis." In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2020. http://dx.doi.org/10.1109/confluence47617.2020.9058024.

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Kambli, Aditi, and Stuti Modi. "Fuzzy Neuro Approach to Water Management Systems." In the 3rd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3310986.3311026.

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Saxena, Urvashi Rahul, and S. P. Singh. "Software effort estimation using Neuro-fuzzy approach." In 2012 CSI Sixth International Conference on Software Engineering (CONSEG). IEEE, 2012. http://dx.doi.org/10.1109/conseg.2012.6349465.

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Bogenberger, K. "A neuro-fuzzy approach for ramp metering." In Tenth International Conference on Road Transport Information and Control. IEE, 2000. http://dx.doi.org/10.1049/cp:20000113.

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Kai, Hongmei, Hongbing Zhu, Kei Eguchi, Zhanyong Guo, Jun Wang, and Hong Zheng. "Application of Neuro-Fuzzy Approach for I2D2RS." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.177.

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Kacmajor, T., and J. J. Michalski. "Neuro-fuzzy approach in microwave filter tuning." In 2011 IEEE/MTT-S International Microwave Symposium - MTT 2011. IEEE, 2011. http://dx.doi.org/10.1109/mwsym.2011.5972771.

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9

Kacmajor, T., and J. J. Michalski. "Neuro-fuzzy approach in microwave filter tuning." In 2011 IEEE/MTT-S International Microwave Symposium - MTT 2011. IEEE, 2011. http://dx.doi.org/10.1109/mwsym.2011.5973241.

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10

Mehdiyev, Nijat Sh, Babek G. Guirimov, and Rafig R. Aliyev. "New neuro-fuzzy approach to recession prediction." In 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control (ICSCCW). IEEE, 2009. http://dx.doi.org/10.1109/icsccw.2009.5379422.

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