Literatura académica sobre el tema "Neuro-Fuzzy Approach"
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Artículos de revistas sobre el tema "Neuro-Fuzzy Approach"
Simiński, Krzysztof. "Neuro-rough-fuzzy approach for regression modelling from missing data". International Journal of Applied Mathematics and Computer Science 22, n.º 2 (1 de junio de 2012): 461–76. http://dx.doi.org/10.2478/v10006-012-0035-4.
Texto completoHan, Man-Wook y Peter Kopacek. "Neuro-Fuzzy Approach in Service Robotics". IFAC Proceedings Volumes 29, n.º 1 (junio de 1996): 760–65. http://dx.doi.org/10.1016/s1474-6670(17)57753-8.
Texto completoRay, Kumar S. y Jayati Ghoshal. "Neuro Fuzzy Approach to Pattern Recognition". Neural Networks 10, n.º 1 (enero de 1997): 161–82. http://dx.doi.org/10.1016/s0893-6080(96)00056-1.
Texto completoBiswas, Saroj, Monali Bordoloi y Biswajit Purkayastha. "Review on Feature Selection and Classification using Neuro-Fuzzy Approaches". International Journal of Applied Evolutionary Computation 7, n.º 4 (octubre de 2016): 28–44. http://dx.doi.org/10.4018/ijaec.2016100102.
Texto completoRutkowska, Danuta y Yoichi Hayashi. "Neuro-Fuzzy Systems Approaches". Journal of Advanced Computational Intelligence and Intelligent Informatics 3, n.º 3 (20 de junio de 1999): 177–85. http://dx.doi.org/10.20965/jaciii.1999.p0177.
Texto completoAmirkhani, Abdollah, Hosna Nasiriyan-Rad y Elpiniki I. Papageorgiou. "A Novel Fuzzy Inference Approach: Neuro-fuzzy Cognitive Map". International Journal of Fuzzy Systems 22, n.º 3 (23 de diciembre de 2019): 859–72. http://dx.doi.org/10.1007/s40815-019-00762-3.
Texto completoNowicki, Robert. "On classification with missing data using rough-neuro-fuzzy systems". International Journal of Applied Mathematics and Computer Science 20, n.º 1 (1 de marzo de 2010): 55–67. http://dx.doi.org/10.2478/v10006-010-0004-8.
Texto completoSadeghi-Niaraki, Abolghasem, Ozgur Kisi y Soo-Mi Choi. "Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods". PeerJ 8 (14 de agosto de 2020): e8882. http://dx.doi.org/10.7717/peerj.8882.
Texto completoSrinivasan, Santhoshkumar y Dhinesh Babu L.D. "A Neuro-Fuzzy Approach to Detect Rumors in Online Social Networks". International Journal of Web Services Research 17, n.º 1 (enero de 2020): 64–82. http://dx.doi.org/10.4018/ijwsr.2020010104.
Texto completoVAIRAPPAN, CATHERINE, SHANGCE GAO, ZHENG TANG y HIROKI TAMURA. "ANNEALED CHAOTIC LEARNING FOR TIME SERIES PREDICTION IN IMPROVED NEURO-FUZZY NETWORK WITH FEEDBACKS". International Journal of Computational Intelligence and Applications 08, n.º 04 (diciembre de 2009): 429–44. http://dx.doi.org/10.1142/s1469026809002680.
Texto completoTesis sobre el tema "Neuro-Fuzzy Approach"
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.
Texto completoOsut, 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.
Texto completoArslan, 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.
Texto completos 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.
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.
Texto completoDesde 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.
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.
Texto completoKim, 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.
Texto completoMountrakis, 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.
Texto completoTaghizadeh, 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.
Texto completoblades 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.
[Verfasser], Habtamu Gezahegn Tolossa y 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.
Texto completoKOTHAMASU, 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.
Texto completoLibros sobre el tema "Neuro-Fuzzy Approach"
Fuzzy-neuro approach to agent applications: From the AI perspective to modern ontology. Berlin: Springer, 2006.
Buscar texto completoLee, R. S. T. Fuzzy-neuro approach to agent applications: From the AI perspective to modern ontology. Berlin, DE: Springer, 2006.
Buscar texto completoJang, Jyh-Shing Roger. Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Upper Saddle River, NJ: Prentice Hall, 1997.
Buscar texto completoLee, Raymond S. T. Fuzzy-Neuro Approach to Agent Applications. Springer, 2005.
Buscar texto completoFuzzy-Neuro Approach to Agent Applications. Berlin/Heidelberg: Springer-Verlag, 2006. http://dx.doi.org/10.1007/3-540-30984-5.
Texto completoLee, Raymond S. T. Fuzzy-Neuro Approach to Agent Applications. Springer, 2008.
Buscar texto completoSun, Chuen-Tsai, Eiji Mizutani y Jyh-Shing Roger Jang. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, 1996.
Buscar texto completoNeuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, 1996.
Buscar texto completoNeuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Pearson Education, Limited, 1996.
Buscar texto completoMinimization of Climatic Vulnerabilities on Mini-hydro Power Plants: Fuzzy AHP, Fuzzy ANP Techniques and Neuro-Genetic Model Approach. Mrinmoy Majumder, 2016.
Buscar texto completoCapítulos de libros sobre el tema "Neuro-Fuzzy Approach"
Lemma, Tamiru Alemu. "Model Identification Using Neuro-Fuzzy Approach". En 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.
Texto completoGianferrara, P., R. Poluzzi y N. Serina. "A Neuro-Fuzzy Approach for Process Modelling". En Fuzzy Logik, 382–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-79386-8_47.
Texto completoSymeonaki, Maria, Aggeliki Kazani y Catherine Michalopoulou. "A Neuro-Fuzzy Approach to Measuring Attitudes". En Demography and Health Issues, 169–81. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76002-5_15.
Texto completoBansal, Ajay Kumar y Swati Mathur. "CBIR Feature Extraction Using Neuro-Fuzzy Approach". En 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.
Texto completoShiu, Simon C. K., X. Z. Wang y Daniel S. Yeung. "Neuro-Fuzzy Approach for Maintaining Case Bases". En Soft Computing in Case Based Reasoning, 259–73. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0687-6_11.
Texto completoTingane, Monali, Amol Bhagat, Priti Khodke y Sadique Ali. "Neuro-Fuzzy Approach for Dynamic Content Generation". En 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.
Texto completoStathacopoulou, Regina, Maria Grigoriadou, George D. Magoulas y Denis Mitropoulos. "A Neuro-fuzzy Approach in Student Modeling". En User Modeling 2003, 337–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44963-9_46.
Texto completoAmadin, Frank Iwebuke y Moses Eromosele Bello. "A Neuro Fuzzy Approach for Predicting Delirium". En 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.
Texto completoRutkowska, Danuta. "Neuro-Fuzzy Architectures Based on the Mamdani Approach". En 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.
Texto completoRutkowska, Danuta. "Neuro-Fuzzy Architectures Based on the Logical Approach". En 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.
Texto completoActas de conferencias sobre el tema "Neuro-Fuzzy Approach"
Tandale, Sayali, Alka S. Barhatte, Rajesh Ghongade y Manisha Dale. "Arrhythmia classification using neuro fuzzy approach". En 2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall). IEEE, 2017. http://dx.doi.org/10.1109/icaccaf.2017.8344712.
Texto completoMorozov, Sergey M. "Neuro-fuzzy Approach for Batteries Depassivation". En 2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2022. http://dx.doi.org/10.1109/elconrus54750.2022.9755528.
Texto completoTyagi, Lakshya y Abhishek Singhal. "Neuro-Fuzzy Approach to Explosion Consequence Analysis". En 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2020. http://dx.doi.org/10.1109/confluence47617.2020.9058024.
Texto completoKambli, Aditi y Stuti Modi. "Fuzzy Neuro Approach to Water Management Systems". En the 3rd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3310986.3311026.
Texto completoSaxena, Urvashi Rahul y S. P. Singh. "Software effort estimation using Neuro-fuzzy approach". En 2012 CSI Sixth International Conference on Software Engineering (CONSEG). IEEE, 2012. http://dx.doi.org/10.1109/conseg.2012.6349465.
Texto completoBogenberger, K. "A neuro-fuzzy approach for ramp metering". En Tenth International Conference on Road Transport Information and Control. IEE, 2000. http://dx.doi.org/10.1049/cp:20000113.
Texto completoKai, Hongmei, Hongbing Zhu, Kei Eguchi, Zhanyong Guo, Jun Wang y Hong Zheng. "Application of Neuro-Fuzzy Approach for I2D2RS". En Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.177.
Texto completoKacmajor, T. y J. J. Michalski. "Neuro-fuzzy approach in microwave filter tuning". En 2011 IEEE/MTT-S International Microwave Symposium - MTT 2011. IEEE, 2011. http://dx.doi.org/10.1109/mwsym.2011.5972771.
Texto completoKacmajor, T. y J. J. Michalski. "Neuro-fuzzy approach in microwave filter tuning". En 2011 IEEE/MTT-S International Microwave Symposium - MTT 2011. IEEE, 2011. http://dx.doi.org/10.1109/mwsym.2011.5973241.
Texto completoMehdiyev, Nijat Sh, Babek G. Guirimov y Rafig R. Aliyev. "New neuro-fuzzy approach to recession prediction". En 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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