Simultaneous Prediction of Wind Speed and Direction by Evolutionary Fuzzy Rule Forest

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@Article{KROMER:2017:PCS,

• author = "Pavel Kromer and Jan Platos",
• title = "Simultaneous Prediction of Wind Speed and Direction by Evolutionary Fuzzy Rule Forest",
• journal = "Procedia Computer Science",
• volume = "108",
• pages = "295--304",
• year = "2017",
• note = "International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland",
• keywords = "genetic algorithms, genetic programming, machine learning, fuzzy rules, compound classifier, forecasting, wind speed, direction",
• ISSN = "1877-0509",
• DOI = "doi:10.1016/j.procs.2017.05.195",
• URL = "http://www.sciencedirect.com/science/article/pii/S187705091730786X",
• 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",
• keywords = "genetic algorithms, genetic programming, machine learning, fuzzy rules, compound classifier, forecasting, wind speed, direction",

}

Genetic Programming entries for Pavel Kromer Jan Platos

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