發(fā)布時(shí)間：2018-09-06 18:13

【摘要】：首先描述基于數(shù)據(jù)挖掘的艦船電力負(fù)荷預(yù)測(cè)系統(tǒng)架構(gòu),然后按照此架構(gòu)進(jìn)行系統(tǒng)實(shí)現(xiàn),并結(jié)合艦船電力負(fù)載預(yù)測(cè)的特點(diǎn),利用遺傳算法獲取較好的搜索空間,這樣可以避免BP神經(jīng)網(wǎng)絡(luò)算法陷入局部最優(yōu)的情況。通過(guò)對(duì)比實(shí)驗(yàn)結(jié)果可知,本文所采用的遺傳算法和BP神經(jīng)網(wǎng)絡(luò)相結(jié)合的優(yōu)化算法預(yù)測(cè)能力強(qiáng),擬合度高。
[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.
【作者單位】： 河南質(zhì)量工程職業(yè)學(xué)院;
【分類號(hào)】：U674.703.3
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本文編號(hào)：2227131