Cost prediction on fabricated substation considering SVM via optimized QPSO
2020
At present, the prediction of the life cycle cost of fabricated substation is
of great significance for the construction of fabricated substation. An
enhanced prediction model based on quantum particle swarm optimization(QPSO)
via least squares support vector machine is established. The relevant
characteristic index of the life cycle of the fabricated substation is used
as the input of the model, and the output is the life cycle cost. The
simulation results are compared with the prediction results of QPSO
optimized LS-SVM, PSO optimized LS-SVM, traditional LS-SVM, and BP neural
network, which shows that the QPSO optimized LS-SVM model has better
prediction accuracy, can predict and evaluate the life cycle cost more
quickly, and can improve the benefits of fabricated substation construction.
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