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An improved load forecasting method of warship based on GA-SVR

机译:基于GA-SVR的军舰载荷预测方法

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An improved forecasting method base on genetic algorithm and support vector machine for warship short-term load forecasting was presented and tested. The new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of genetic algorithm and Support vector machine was disscused first, and the method of genetic algorithm was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive GA optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive GA-SVM method. finally Through simulation results show that the adaptive GA-SVM method is highly feasible to predict with high accuracy and high generalization capability.
机译:提出和测试了一种改进的遗传算法的预测方法基础,并测试了战舰短期负荷预测的支持。 战舰电力负荷的新影响因素用于建模,不同于土地网格和民用船只网格。 首先对遗传算法和支持向量机进行遗传算法理论,提高了遗传算法的方法,具有自适应参数优化的能力。 通过自适应GA优化方法改善了支持向量机的方法。 然后通过自适应GA-SVM方法建立了一种新的自适应短期负荷预测模型。 最后通过仿真结果表明,自适应GA-SVM方法以高精度和高概括能力预测是高度可行的。

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