数据挖掘在舰船电力负荷预测中的应用研究
发布时间：2018-09-06 18:13
【摘要】：首先描述基于数据挖掘的舰船电力负荷预测系统架构,然后按照此架构进行系统实现,并结合舰船电力负载预测的特点,利用遗传算法获取较好的搜索空间,这样可以避免BP神经网络算法陷入局部最优的情况。通过对比实验结果可知,本文所采用的遗传算法和BP神经网络相结合的优化算法预测能力强,拟合度高。
[Abstract]:Firstly, the architecture of ship power load forecasting system based on data mining is described, and then the system is implemented according to this architecture. Combining with the characteristics of ship power load forecasting, genetic algorithm is used to obtain a better search space. In this way, the BP neural network algorithm can be avoided from falling into local optimal condition. By comparing the experimental results, it can be seen that the genetic algorithm combined with BP neural network has strong predictive ability and high fitting degree.
【作者单位】： 河南质量工程职业学院;
【分类号】：U674.703.3
，
本文编号：2227131
[Abstract]:Firstly, the architecture of ship power load forecasting system based on data mining is described, and then the system is implemented according to this architecture. Combining with the characteristics of ship power load forecasting, genetic algorithm is used to obtain a better search space. In this way, the BP neural network algorithm can be avoided from falling into local optimal condition. By comparing the experimental results, it can be seen that the genetic algorithm combined with BP neural network has strong predictive ability and high fitting degree.
【作者单位】： 河南质量工程职业学院;
【分类号】：U674.703.3
，
本文编号：2227131
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