Academic literature on the topic 'Fuzzy GMDH'
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Journal articles on the topic "Fuzzy GMDH"
Ozovehe, Aliyu, Okpo U. Okereke, Anene E. Chibuzo, and Abraham U. Usman. "Comparative Analysis of Traffic Congestion Prediction Models for Cellular Mobile Macrocells." European Journal of Engineering Research and Science 3, no. 6 (June 30, 2018): 32. http://dx.doi.org/10.24018/ejers.2018.3.6.767.
Full textOzovehe, Aliyu, Okpo U. Okereke, Anene E. Chibuzo, and Abraham U. Usman. "Comparative Analysis of Traffic Congestion Prediction Models for Cellular Mobile Macrocells." European Journal of Engineering and Technology Research 3, no. 6 (June 30, 2018): 32–38. http://dx.doi.org/10.24018/ejeng.2018.3.6.767.
Full textZaychenko, Yuriy, and Helen Zaychenko. "Fuzzy GMDH and its application to forecasting financial processes." System research and information technologies, no. 1 (March 25, 2019): 91–109. http://dx.doi.org/10.20535/srit.2308-8893.2019.1.07.
Full textMohanty, Ramakanta, V. Ravi, and M. R. Patra. "Application of Machine Learning Techniques to Predict Software Reliability." International Journal of Applied Evolutionary Computation 1, no. 3 (July 2010): 70–86. http://dx.doi.org/10.4018/jaec.2010070104.
Full textYousefpour, A., and Z. Ahmadpour. "The Prediction Of Air Pollution By Using Neuro-fuzzy Gmdh." Journal of Mathematics and Computer Science 02, no. 03 (April 15, 2011): 488–94. http://dx.doi.org/10.22436/jmcs.02.03.13.
Full textNAGASAKA, K., H. ICHIHASHI, and R. LEONARD. "Neuro-fuzzy GMDH and its application to modelling grinding characteristics." International Journal of Production Research 33, no. 5 (May 1995): 1229–40. http://dx.doi.org/10.1080/00207549508930206.
Full textZhu, Bing, Chang-Zheng He, Panos Liatsis, and Xiao-Yu Li. "A GMDH-based fuzzy modeling approach for constructing TS model." Fuzzy Sets and Systems 189, no. 1 (February 2012): 19–29. http://dx.doi.org/10.1016/j.fss.2011.08.004.
Full textHayashi, Isao, and Hideo Tanaka. "The fuzzy GMDH algorithm by possibility models and its application." Fuzzy Sets and Systems 36, no. 2 (June 1990): 245–58. http://dx.doi.org/10.1016/0165-0114(90)90182-6.
Full textHeydari, Azim, Meysam Majidi Nezhad, Mehdi Neshat, Davide Astiaso Garcia, Farshid Keynia, Livio De Santoli, and Lina Bertling Tjernberg. "A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data." Energies 14, no. 12 (June 11, 2021): 3459. http://dx.doi.org/10.3390/en14123459.
Full textHarandizadeh, Hooman. "Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 34, no. 1 (January 30, 2020): 114–26. http://dx.doi.org/10.1017/s0890060420000025.
Full textDissertations / Theses on the topic "Fuzzy GMDH"
GONCALVES, IRACI M. P. "Monitoração e diagnóstico para detecção de falhas de sensores utilizando a metodologia GMDH." reponame:Repositório Institucional do IPEN, 2006. http://repositorio.ipen.br:8080/xmlui/handle/123456789/11382.
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Tese (Doutoramento)
IPEN/T
Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP
Gonçalves, Iraci Martinez Pereira. "Monitoração e diagnóstico para detecção de falhas de sensores utilizando a metodologia GMDH." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/85/85133/tde-04062012-144516/.
Full textThe fault detection and diagnosis system is an Operator Support System dedicated to specific functions that alerts operators to sensors and actuators fault problems, and guide them in the diagnosis before the normal alarm limits are reached. Operator Support Systems appears to reduce panels complexity caused by the increase of the available information in nuclear power plants control room. In this work a Monitoring and Diagnosis System was developed based on the GMDH (Group Method of Data Handling) methodology. The methodology was applied to the IPEN research reactor IEA-R1. The system performs the monitoring, comparing GMDH model calculated values with measured values. The methodology developed was firstly applied in theoretical models: a heat exchanger model and an IPEN reactor theoretical model. The results obtained with theoretical models gave a base to methodology application to the actual reactor operation data. Three GMDH models were developed for actual operation data monitoring: the first one using just the thermal process variables, the second one was developed considering also some nuclear variables, and the third GMDH model considered all the reactor variables. The three models presented excellent results, showing the methodology utilization viability in monitoring the operation data. The comparison between the three developed models results also shows the methodology capacity to choose by itself the best set of input variables for the model optimization. For the system diagnosis implementation, faults were simulated in the actual temperature variable values by adding a step change. The fault values correspond to a typical temperature descalibration and the result of monitoring faulty data was then used to build a simple diagnosis system based on fuzzy logic.
Нафас, Агаї Аг Гаміш Ові. "Прогнозування ризику банкрутства в промисловій та банківській сфері з використанням нечітких моделей та алгоритмів." Thesis, НТУУ "КПІ", 2016. https://ela.kpi.ua/handle/123456789/14938.
Full textThe thesis is devoted to the development of models and algorithms for analysis of financial state and forecasting of bankruptcy risk of enterprises and banks in condition of uncertainty, incomplete and unreliable information on the example of the Ukrainian economy. Classical statistical methods for predicting the risk of bankruptcy on the basis of multivariate discriminant analysis, in particular the method of Altman, are analyzed. It revealed its deficiencies and inappropriateness of its use in Ukraine's economy, since it is based on the use of reliable information on the state enterprises. Therefore, the use of fuzzy neural networks (FNN) with the conclusions Mamdani and Tsukamoto to forecast the risk of bankruptcy in the conditions of incompleteness and uncertainty is entirely justified. In the thesis rule base is developed for solving the problem of financial analysis and forecasting the risk of bankruptcy of enterprises for neural networks Mamdani and Tsukamoto. Since the total size of the comprehensive fuzzy rule base is great that does not allow its training in a short time, a method of reducing the size of the rule base and its visual representation through the use of scores is suggested. Algorithms for predicting the risk of bankruptcy of enterprises with FNN Mamdani and Tsukamoto are developed. Further in the paper the cascade neo-fuzzy network (CNFN) for predicting the risk of bankruptcy in condition of uncertainty is suggested. Its features is the absence of the rule base, as well as the fact that the membership functions are fixed and does not need training. Therefore, these networks have accelerated the convergence of training compared with FNN Mamdani and Tsukamoto. Experimental studies of the proposed models and algorithms for the forecasting of the risk of bankruptcy in Ukraine and comparative analysis with classical methods are presented. The experimental results showed that the accuracy of predicting the bankruptcy risk by Altmana- by 68- 70%, matrix method - 80%, cascade neo-fuzzy neural network - 87% and FNN Mamdanі and Tsukamoto - 88-90%. The paper also studied the problem of forecasting the risk of bankruptcy in the banking sector of Ukraine in conditions of uncertainty. To solve this problem using FNN TSK and ANFIS is proposed. Experimental research of effectiveness of using FNN to predict the risk of bank failures and comparison with statistical models ARIMA, logit-model, probit-model and fuzzy GMDH are presented. The experiment established that the greatest prediction accuracy allows the use of FNN TSK (2%) and fuzzy GMDH (4%), while the statistical models: logit-model - 16%, probit-model - 14% and ARIMA - 18%. During the experiments adequate financial and economic indicators of banks to predict the risk of bankruptcy were determined.
Диссертация посвящена разработке моделей и алгоритмов анализа финансового состояния и прогнозирования риска банкротства предприятий и банков в условиях неопределенности, неполной и недостоверной информации на примере экономики Украины. Проанализированы классические статистические методы прогнозирования риска банкротства предприятий на основе методов многомерного дискриминантного анализа, в частности метод Альтмана. Выявлено его недостатки и нецелесообразность использования в условиях экономики Украины, поскольку он базируется на использовании достоверной информации о состоянии предприятий. Поэтому в работе обосновано использование для прогнозирования риска банкротства в условиях неполноты и неопределенности нечетких нейронных сетей (ННС) с выводами Мамдани и Цукамото. В дисертации разработана база правил для решения задачи анализа финансового состояния и прогнозирования риска банкротства предприятий в условиях неопределенности для нейросетей Мамдани и Цукамото. Поскольку общий размер полной базы нечетких правил большой, что не дает возможности ее обучения за короткое время, предложен способ сокращения размеров базы правил и ее наглядное представление путем использования балльных оценок. Разработаны алгоритмы прогнозирования риска банкротства предприятий с использованием ННС Мамдани и Цукамото. Далее в работе рассмотрены каскадные нео-фаззи сети для прогнозирования риска банкротства предприятий в условиях неопределенности. Их особенностями является отсутствие базы правил вывода, а также то, что функции принадлежностей фиксированные и не нуждаются в обучении, обучаются лишь линейные параметры – веса связей ННС. Поэтому эти сети имеют ускоренную сходимость обучения в сравнении с ННС Мамдани и Цукамото. Проведены экспериментальные исследования предложенных моделей и алгоритмов для прогнозирования риска банкротства предприятий Украины и сравнительный анализ с классическими методами. Результаты экспериментов показали, что точность прогнозирования риска банкротства составляет методом Альтмана - 68-70%, матричным методом - 80%, нео-фаззи каскадной нейросетью - 87%, а ННМ Мамдани и Цукамото -88-90 %. В работе также была исследована проблема прогнозирования риска банкротства в банковской сфере Украины в условиях неопределенности. Для решения этой проблемы предложено использование ННС TSK и ANFIS. Проведены экспериментальные исследования эффективности использования ННС для прогнозирования риска банкротства банков и сравнение со статистическими моделями ARIMA, logit-model и probit–model, а также с нечетким МГУА. В результате экспериментов установлено, что самую большую точность прогнозирования обеспечивает использование ННМ TSK (2%) и нечеткий МГУА (4%), тогда как статистические модели имеют точность: logit-model - 16%, probit–model - 14% и ARIMA - 18%. В процессе экспериментов были также определены адекватные финансово-экономические показатели банков для прогнозирования риска банкротства.
Heinrich, Kenneth. "FLoRa ein auf Fuzzy-Logik basierender Beitrag zur ganzheitlichen Bewertung und transparenteren Verrechnung von Rüstaufwänden beim Verpacken automobiler Ersatzteile ; verdeutlicht am Beispiel des Automotive Aftermarket der Robert-Bosch-GmbH." Aachen Shaker, 2009. http://d-nb.info/997951206/04.
Full textHeinrich, Kenneth [Verfasser]. "FLoRa. Ein auf Fuzzy Logik basierender Beitrag zur ganzheitlichen Bewertung und transparenteren Verrechnung von Rüstaufwänden beim Verpacken automobiler Ersatzteile : Verdeutlicht am Beispiel des Automotive Aftermarket der Robert Bosch GmbH / Kenneth Heinrich." Aachen : Shaker, 2009. http://d-nb.info/1159836744/34.
Full textSteinebach, Mario, Alexander Friebel, Christine Häckel-Riffler, Volker Tzschucke, Wolfram Dötzel, Egon Müller, Thomas Gäse, et al. "TU-Spektrum "Sonderausgabe Auto & Verkehr" 2004, Magazin der Technischen Universität Chemnitz." Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200400909.
Full textBook chapters on the topic "Fuzzy GMDH"
Saha, Apu K., Deepjyoti Deb, and Prachi D. Khobragade. "Power Allocation in an Educational Institute in India: A Fuzzy-GMDH Approach." In Water and Energy Management in India, 221–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66683-5_11.
Full textZgurovsky, Michael Z., and Yuriy P. Zaychenko. "Deep Neural Networks and Hybrid GMDH-Neuro-fuzzy Networks in Big Data Analysis." In Studies in Big Data, 43–95. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14298-8_2.
Full textDu, Wenli, and Feng Qian. "Optimization of PTA Crystallization Process Based on Fuzzy GMDH Networks and Differential Evolutionary Algorithm." In Lecture Notes in Computer Science, 631–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539117_90.
Full textBodyanskiy, Yevgeniy, Yuriy Zaychenko, Olena Boiko, Galib Hamidov, and Anna Zelikman. "Structure Optimization and Investigations of the Hybrid GMDH-Neo-fuzzy Neural Networks in Forecasting Problems." In Studies in Computational Intelligence, 209–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94910-5_12.
Full textBodyanskiy, Yevgeniy, Olena Vynokurova, and Oleksii Tyshchenko. "Hybrid Wavelet-Neuro-Fuzzy Systems of Computational Intelligence in Data Mining Tasks." In Handbook of Research on Machine Learning Innovations and Trends, 787–825. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2229-4.ch035.
Full textMohanty, Ramakanta, V. Ravi, and M. R. Patra. "Application of Machine Learning Techniques to Predict Software Reliability." In Machine Learning, 354–70. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch301.
Full textDu, Wenli, Zhiming Liu, Feng Qian, Mandan Liu, and Kai Zhang. "4-CBA online soft-measurement via fuzzy GMDH networks." In Computer Aided Chemical Engineering, 1268–73. Elsevier, 2003. http://dx.doi.org/10.1016/s1570-7946(03)80484-4.
Full textConference papers on the topic "Fuzzy GMDH"
Bodyanskiy, Yevgeniy, Olena Boiko, Yuriy Zaychenko, and Galib Hamidov. "Evolving Hybrid GMDH-Neuro-Fuzzy Network and its Applications." In 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC). IEEE, 2018. http://dx.doi.org/10.1109/saic.2018.8516755.
Full textHengjun Zhao, Changzheng He, and Zhen Ye. "Fuzzy Clustering-Based GMDH Model to Feature Selection in Customer Analysis." In 2008 International Seminar on Business and Information Management (ISBIM 2008). IEEE, 2008. http://dx.doi.org/10.1109/isbim.2008.116.
Full textBodyanskiy, Yevgeniy, Olena Boiko, Yuriy Zaychenko, and Galib Hamidov. "Evolving GMDH-neuro-fuzzy system with small number of tuning parameters." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8392957.
Full textChen, Hong, and Senfa Chen. "A Fuzzy GMDH Network and its Application in Traffic Flow Forecasting." In 2009 International Joint Conference on Artificial Intelligence (JCAI). IEEE, 2009. http://dx.doi.org/10.1109/jcai.2009.37.
Full textHo Sung Park, Sung Kwun Oh, Tae Chon Ahn, and W. Pedrycz. "A study on multi-layer fuzzy polynomial inference system based on an extended GMDH algorithm." In Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.793265.
Full textHwang, Heung-Suk, Suk-Tae Bae, and Gyu-Sung Cho. "Container Terminal Demand Forecasting Framework Using Fuzzy-GMDH and Neural Network Method." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.225.
Full textZaychenko, Yuriy, and Galib Hamidov. "The Hybrid Deep Learning GMDH-neo-fuzzy Neural Network and Its Applications." In 2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2019. http://dx.doi.org/10.1109/aict47866.2019.8981725.
Full textBodyanskiy, Yevgeniy, Olena Boiko, Yuriy Zaychenko, Galib Hamidov, and Anna Zelikman. "The Hybrid GMDH-Neo-fuzzy Neural Network in Forecasting Problems in Financial Sphere." In 2020 IEEE 2nd International Conference on System Analysis & Intelligent Computing (SAIC). IEEE, 2020. http://dx.doi.org/10.1109/saic51296.2020.9239152.
Full textQin, Yechen, Reza Langari, and Liang Gu. "The Use of Vehicle Dynamic Response to Estimate Road Profile Input in Time Domain." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-5978.
Full textMenezes, Rafael Abud, and Julio Cesar Nievola. "RCMDE-GMD: Predicting gene ontology terms using differential evolution." In 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2014. http://dx.doi.org/10.1109/fskd.2014.6980888.
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