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Improvement of Genetic Programming Based Nonlinear Regression Using ADF and Application for Prediction MOS of Wind Speed
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 Title & Authors
Improvement of Genetic Programming Based Nonlinear Regression Using ADF and Application for Prediction MOS of Wind Speed
Oh, Seungchul; Seo, Kisung;
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A linear regression is widely used for prediction problem, but it is hard to manage an irregular nature of nonlinear system. Although nonlinear regression methods have been adopted, most of them are only fit to low and limited structure problem with small number of independent variables. However, real-world problem, such as weather prediction required complex nonlinear regression with large number of variables. GP(Genetic Programming) based evolutionary nonlinear regression method is an efficient approach to attach the challenging problem. This paper introduces the improvement of an GP based nonlinear regression method using ADF(Automatically Defined Function). It is believed ADFs allow the evolution of modular solutions and, consequently, improve the performance of the GP technique. The suggested ADF based GP nonlinear regression methods are compared with UM, MLR, and previous GP method for 3 days prediction of wind speed using MOS(Model Output Statistics) for partial South Korean regions. The UM and KLAPS data of 2007-2009, 2011-2013 years are used for experimentation.
Genetic programming;Automatically defined function;Wind speed prediction;MOS(Model output statistics);
 Cited by
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