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• W2783006481 abstract "Accurate prediction of substation project cost is helpful to improve the investment management and sustainability. It is also directly related to the economy of substation project. Ensemble Empirical Mode Decomposition (EEMD) can decompose variables with non-stationary sequence signals into significant regularity and periodicity, which is helpful in improving the accuracy of prediction model. Adding the Gauss perturbation to the traditional Cuckoo Search (CS) algorithm can improve the searching vigor and precision of CS algorithm. Thus, the parameters and kernel functions of Support Vector Machines (SVM) model are optimized. By comparing the prediction results with other models, this model has higher prediction accuracy." @default.
• W2783006481 created "2018-01-26" @default.
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• W2783006481 date "2018-01-05" @default.
• W2783006481 modified "2023-09-26" @default.
• W2783006481 title "Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines" @default.
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• W2783006481 doi "https://doi.org/10.3390/su10010118" @default.
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