Abstract
Using the global optimization properties of Particle Swarm Optimization(PSO) to carry out parameter identification of support vector machine(SVM). Before the particle swarm search for parameters, exponential transform the parameters first to make intervals [0, 1] and [1, infinity] have the same search probability. Fitness function of PSO as generalization ability of support vector machine model to be the standard, at the same time discussed the minimum error of testing samples and leave-one-out method to the SVM learning method promotion ability. Finally taking the data of monthly runoff of Yichang station in Yangtze River as an example, respectively using the ARMA model, seasonal ARIMA model, BP neural network model and the SVM model that have built to simulate forecasting, the result shows the validity of the model.
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Yong, P., Zhi-chun, X. (2010). Research of Long-Term Runoff Forecast Based on Support Vector Machine Method. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16527-6_17
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DOI: https://doi.org/10.1007/978-3-642-16527-6_17
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