Abstract
Support vector regression (SVR) is an important machine learning algorithm, although some successful applications have been achieved. The algorithm for complex system is still worth studying. Multiple output intuitionistic fuzzy least squares support vector regression (IFLS-SVR) is improved by using the intuitionistic fuzzy to solve the problem of the uncertain multiple output complex system. Compared with the traditional fuzzy support vector regression, the model with the fuzzy membership and non-fuzzy membership is more close to the practical system. Multiple output IFLS-SVR transforms the actual data into fuzzy data and transforms the quadratic programming optimization problem into a series of linear equations. Compared with the current fuzzy support vector regression, multiple output IFLS-SVR in this paper adopted the intuitionistic fuzzy method to calculate membership functions, improving the training efficiency of the algorithm and reducing the training time by using the least square method. Through the simulation model, multiple output IFLS-SVR has achieved good results compared with other methods. The application of multiple output IFLS-SVR to the prediction of complex wind weather has also achieved good results.
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This work is supported by the National Natural Science Foundation of China (No. 61103141).
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Wang, D., Lu, Y., Chen, B., Chen, L. (2018). Research on Intuitionistic Fuzzy Multiple Output Least Squares Support Vector Regression. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_36
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DOI: https://doi.org/10.1007/978-3-030-00009-7_36
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