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针对舰船装备临修经费需求预测得不到满意解的问题,运用遗传算法将SVM相应的参数进行优化,建立了基于GA-SVM的舰船装备临修经费预测模型.通过将GA-SVM模型与BP神经网络模型的预测结果进行对比分析,结果表明:GASVM的预测效果更优异,对舰船装备临修经费需求预测有更好的参考意义.
In order to solve the problem that the demand for temporary equipment for ship equipment can not be satisfactorily solved, genetic algorithm is used to optimize the parameters of SVM, and a GA-SVM model is established to estimate the temporary funding for warship equipment. Compared with the BP neural network model, the results show that the GASVM has better forecasting effect, which has a better reference value for the forecasted funding requirement of the warship equipment.