Simultaneous Prediction of Wind Speed and Direction by Evolutionary Fuzzy Rule Forest
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- @Article{KROMER:2017:PCS,
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author = "Pavel Kromer and Jan Platos",
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title = "Simultaneous Prediction of Wind Speed and Direction by
Evolutionary Fuzzy Rule Forest",
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journal = "Procedia Computer Science",
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volume = "108",
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pages = "295--304",
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year = "2017",
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note = "International Conference on Computational Science,
ICCS 2017, 12-14 June 2017, Zurich, Switzerland",
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keywords = "genetic algorithms, genetic programming, machine
learning, fuzzy rules, compound classifier,
forecasting, wind speed, direction",
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ISSN = "1877-0509",
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DOI = "doi:10.1016/j.procs.2017.05.195",
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URL = "http://www.sciencedirect.com/science/article/pii/S187705091730786X",
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abstract = "An accurate estimate of wind speed and direction is
important for many application domains including
weather prediction, smart grids, and e.g. traffic
management. These two environmental variables depend on
a number of factors and are linked together.
Evolutionary fuzzy rules, based on fuzzy information
retrieval and genetic programming, have been used to
solve a variety of real-world regression and
classification tasks. They were, however, limited by
the ability to estimate only one variable by a single
model. In this work, we introduce an extended version
of this predictor that facilitates an artificial
evolution of forests of fuzzy rules. In this way,
multiple variables can be predicted by a single model
that is able to comprehend complex relations between
input and output variables. The usefulness of the
proposed concept is demonstrated by the evolution of
forests of fuzzy rules for simultaneous wind speed and
direction prediction",
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keywords = "genetic algorithms, genetic programming, machine
learning, fuzzy rules, compound classifier,
forecasting, wind speed, direction",
- }
Genetic Programming entries for
Pavel Kromer
Jan Platos
Citations