【摘要】：隨著我國(guó)航運(yùn)業(yè)的快速發(fā)展,海上交通變得越來(lái)越發(fā)達(dá)的同時(shí),海上交通事故也逐漸增多�？茖W(xué)而準(zhǔn)確的船舶交通流量預(yù)測(cè)能為海事機(jī)關(guān)和港航部門制定港口和航道規(guī)劃提供數(shù)據(jù)支持和理論依據(jù),是減少海上交通事故的關(guān)鍵因素之一。本文在總結(jié)現(xiàn)有船舶交通流量預(yù)測(cè)模型的基礎(chǔ)上,對(duì)神經(jīng)網(wǎng)絡(luò)船舶交通流量預(yù)測(cè)模型進(jìn)行研究,并提出基于遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的船舶交通流量預(yù)測(cè)模型。首先,簡(jiǎn)述海上交通流的理論基礎(chǔ)知識(shí)和船舶交通流量預(yù)測(cè)的基本概念,并給出船舶交通流量預(yù)測(cè)的評(píng)價(jià)指標(biāo)。其次,為減少數(shù)據(jù)波動(dòng)對(duì)預(yù)測(cè)精度的影響,運(yùn)用五點(diǎn)三次平滑處理方法對(duì)采集的數(shù)據(jù)進(jìn)行平滑處理和歸一化處理。然后,建立基于BP神經(jīng)網(wǎng)絡(luò)的船舶交通流量預(yù)測(cè)模型,針對(duì)BP神經(jīng)網(wǎng)絡(luò)的固有缺陷,應(yīng)用遺傳算法對(duì)BP神經(jīng)網(wǎng)絡(luò)進(jìn)行優(yōu)化,建立基于遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的船舶交通流量預(yù)測(cè)模型。最后,分別采用BP神經(jīng)網(wǎng)絡(luò)模型和遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)模型對(duì)深圳港的船舶交通流量進(jìn)行預(yù)測(cè)。結(jié)果表明,在一定誤差范圍內(nèi),BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型和遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型能較好的預(yù)測(cè)深圳港的船舶交通流量。對(duì)比分析上述兩預(yù)測(cè)模型的預(yù)測(cè)結(jié)果,分析結(jié)果表明遺傳算法能夠有效避免BP神經(jīng)網(wǎng)絡(luò)的固有缺陷,應(yīng)用遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的船舶交通流量預(yù)測(cè)模型的預(yù)測(cè)精度更高,誤差更小。
[Abstract]:With the rapid development of China's shipping industry, maritime traffic becomes more and more developed, and maritime traffic accidents are gradually increasing. Scientific and accurate prediction of ship traffic flow can provide data support and theoretical basis for maritime authorities and port and shipping departments to formulate port and channel planning. It is one of the key factors to reduce maritime traffic accidents. On the basis of summarizing the existing ship traffic flow forecasting model, this paper studies the ship traffic flow forecasting model based on neural network, and puts forward a ship traffic flow forecasting model based on genetic algorithm to optimize BP neural network. Firstly, the basic theoretical knowledge of marine traffic flow and the basic concept of ship traffic flow forecasting are briefly introduced, and the evaluation indexes of ship traffic flow prediction are given. Secondly, in order to reduce the influence of data fluctuation on prediction accuracy, 5.3 times smoothing processing method is used to smooth and normalize the collected data. Then, the ship traffic flow forecasting model based on BP neural network is established. Aiming at the inherent defects of BP neural network, the genetic algorithm is applied to optimize the BP neural network. A ship traffic flow forecasting model based on genetic algorithm (GA) optimization BP neural network is established. Finally, the BP neural network model and the genetic algorithm optimization BP neural network model are used to predict the ship traffic flow in Shenzhen Port. The results show that BP neural network prediction model and genetic algorithm optimization BP neural network prediction model can better predict the ship traffic flow in Shenzhen Port within a certain range of errors. The results show that genetic algorithm can effectively avoid the inherent defects of BP neural network, and the prediction accuracy of ship traffic flow forecasting model based on genetic algorithm optimization of BP neural network is higher. The error is smaller.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U692

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本文編號(hào)：2365763