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Fuzzy adaptive nonlinear MIMO control for rigid coupled multibody robots using reinforcement learning model. [PDF]
Sci RepDuan C, Wang L, Li S.
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Temporal Properties of Cardiorespiratory Coupling in Patients with Heart Failure During the Circadian Cycle. [PDF]
Entropy (Basel)Buitrago-Ricaurte N +5 more
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Incremental fuzzy clustering of time series
Fuzzy Sets and Systems, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ling Wang, Peipei Xu, Qian Ma
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Parsimonious fuzzy time series modelling
Expert Systems with Applications, 2020Abstract This paper proposes a novel modelling structure to ensure the parsimony of fuzzy time series (FTS) models while retaining certain level of out-of-sample accuracy. A parsimonious FTS model requires multiple optimizations of hyper-parameters such as time lags and partitioning which consists of the number of fuzzy sets, the partitioning type ...
Ruobin Gao, Okan Duru
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Fuzzy forecasting based on fuzzy time series
International Journal of Computer Mathematics, 2004This article presents an improved method of fuzzy time series to forecast university enrollments. The historical enrollment data of the University of Alabama were first adopted by Song and Chissom (Song, Q. and Chissom, B. S. (1993). Forecasting enrollment with fuzzy time series-part I, Fuzzy Sets and Systems, 54, 1–9; Song, Q. and Chissom, B. S. (1994)
Hsuan-Shih Lee, Ming-Tao Chou
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Interval forecasting with Fuzzy Time Series
2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016In recent years, the demand for developing low computational cost methods to deal with uncertainty in forecasting has been increased. Interval forecasting is a category of forecasting in which the method provides intervals as outputs of its forecasting.
Petrônio C. L. Silva +2 more
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Probabilistic Forecasting With Fuzzy Time Series
IEEE Transactions on Fuzzy Systems, 2020In recent years, the demand for developing low computational cost methods to deal with uncertainties in forecasting has been increased. Probabilistic forecasting is a class of forecasting in which the method provides intervals or probability distributions as outcomes of its forecasting.
Petrônio Cândido de Lima e Silva +3 more
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Introducing polynomial fuzzy time series
Journal of Intelligent & Fuzzy Systems, 2013Using polynomial concept and non-liner optimization enhanced the performance of Chen's (1996) and Yu's (2005b) methods as the two frequently used methods in fuzzy time series model. To this end, polynomial schemes were given to each fuzzy logical relationship groups that had been established through forecast process to establish non-linear optimization
Muhammad Hisyam Lee +2 more
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Fuzzy Transforms and Seasonal Time Series
2017Like in our previous papers, we show the trend of seasonal time series by means of polynomial interpolation and we use the inverse fuzzy transform for prediction of the value of an assigned output. As example, we use the daily weather dataset of the city of Naples (Italy) starting from data collected from 2003 till to 2015 making predictions on the the
salvatore sessa, ferdinando di martino
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Fuzzy classification of time series data
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013The problem of classification of time series data is an interesting problem in the field of data mining. Even though several algorithms have been proposed for the problem of time series classification we have developed an innovative algorithm which is computationally fast and accurate in several cases when compared with 1NN classifier. In our method we
Penugonda Ravikumar, V. Susheela Devi
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