本文選題：BP神經(jīng)網(wǎng)絡(luò) + 人工蜂群算法�。� 參考：《大連海事大學》2017年碩士論文

【摘要】：引航量是指到港船舶的引航艘次,是港口發(fā)展建設(shè)綜合評估中的一個重要指標。引航工作是港口安全和服務(wù)不可或缺的重要環(huán)節(jié),是國際航運的重要組成部分和水上國門第一形象。引航安全關(guān)系到國際聲譽、主權(quán)維護和政府形象,關(guān)系到人命財產(chǎn)和水域環(huán)境的安全。引航量的預(yù)測研究能夠為各個引航站的發(fā)展規(guī)劃和人力資源布局提供合理的數(shù)據(jù)、決策支持,從而使得港口能夠高速、安全的發(fā)展。在人工神經(jīng)網(wǎng)絡(luò)的應(yīng)用中,BP神經(jīng)網(wǎng)絡(luò)是其中應(yīng)用最為廣泛的一種,但是在BP神經(jīng)網(wǎng)絡(luò)模型中由于其自身的原因造成BP神經(jīng)網(wǎng)絡(luò)在訓(xùn)練學習過程中存在容易陷入局部最小值、收斂速度慢等不足之處。人工蜂群算法是模擬蜜蜂采蜜的過程,該算法是根據(jù)蜜蜂采蜜的原理以及蜜蜂之間相互交流的方式而提出的一種新的智能算法,人工蜂群算法具有較強的全局和局部搜索能力,利用該優(yōu)點,可以將BP神經(jīng)網(wǎng)絡(luò)求解各層最優(yōu)的權(quán)值和閥值的這一過程轉(zhuǎn)化為蜜蜂采蜜過程中搜尋最佳蜜源的過程,并應(yīng)用于引航量的預(yù)測。本文首先介紹了人工神經(jīng)網(wǎng)絡(luò)以及人工蜂群算法,然后通過查閱參考文獻和采用專家調(diào)查表法確定了影響引航量的因素,包括有:GDP、港口吞吐量、固定資產(chǎn)投資額。在此基礎(chǔ)建立了 ABC-BP引航量預(yù)測模型,并與BP神經(jīng)網(wǎng)絡(luò)訓(xùn)練以及驗證預(yù)測結(jié)果進行了分析對比,得出ABC-BP神經(jīng)網(wǎng)絡(luò)模型訓(xùn)練過程穩(wěn)定,誤差較小,并且訓(xùn)練過程中收斂速度較快,該模型穩(wěn)定可靠。以深圳港引航站為實例,利用ABC-BP神經(jīng)網(wǎng)絡(luò)對深圳港進行引航量預(yù)測研究,通過調(diào)查問卷確定影響深圳港引航量因素,設(shè)置相關(guān)參數(shù),以2001-2013年深圳港引航站引航量為訓(xùn)練樣本,2014-2016年引航量為驗證樣本。最后利用已訓(xùn)練好的ABC-BP神經(jīng)網(wǎng)絡(luò)以及各因素平均增長率,對深圳港2020年和2030年的引航量進行了預(yù)測。
[Abstract]:Pilotage refers to the pilotage of ships arriving at port and is an important index in the comprehensive evaluation of port development and construction. Pilotage is an indispensable part of port security and service, an important part of international shipping and the first image of the waterfront. Pilotage safety is related to international reputation, sovereignty maintenance, government image, life and property, and the safety of water environment. The prediction of pilotage can provide reasonable data and decision support for the development planning and human resource layout of each pilot station, so that the port can develop at a high speed and safely. In the application of artificial neural network, BP neural network is one of the most widely used, but in the BP neural network model, because of its own reasons, the BP neural network is prone to fall into local minimum value in the process of training and learning. The convergence speed is slow and so on. Artificial bee colony algorithm is a new intelligent algorithm based on the principle of honeybee honey collection and the way of honeybee communication. Artificial bee colony algorithm has strong global and local search ability. By using this advantage, the process of solving the optimal weight and threshold value of each layer by BP neural network can be transformed into the process of searching for the best honey source in the honeybee honey harvesting process, and applied to the prediction of pilotage. In this paper, the artificial neural network and the artificial bee colony algorithm are introduced at first, and then the factors affecting pilotage are determined by reference and expert questionnaire, including the proportion of GDP, port throughput and the investment of fixed assets. On this basis, the prediction model of ABC-BP pilotage is established and compared with BP neural network training and verification and prediction results. It is concluded that the training process of ABC-BP neural network model is stable, the error is small, and the convergence rate of the model is faster during the training process. The model is stable and reliable. Taking the pilotage station of Shenzhen Port as an example, using ABC-BP neural network to study the pilotage prediction of Shenzhen Port, the factors affecting pilotage volume of Shenzhen Port are determined by questionnaire, and the relevant parameters are set up. The pilotage of Shenzhen Port from 2001 to 2013 is taken as the training sample and the pilotage from 2014-2016 as the verification sample. Finally, using the trained ABC-BP neural network and the average growth rate of various factors, the pilotage of Shenzhen Port in 2020 and 2030 is forecasted.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U692

【參考文獻】

相關(guān)期刊論文 前10條

1 梁方楚,張智煥;基于動態(tài)BP神經(jīng)網(wǎng)絡(luò)的非線性預(yù)測及在港口貨物吞吐量中的應(yīng)用[J];運籌與管理;1998年04期

2 張雅波,張躍龍;定性預(yù)測方法[J];吉林建筑工程學院學報;1999年01期

3 趙一飛;上海國際集裝箱樞紐港備選方案比較[J];上海交通大學學報;2000年01期

4 黃榮富,真虹;三次指數(shù)平滑法在港口吞吐量預(yù)測中的應(yīng)用[J];水運管理;2003年02期

5 朱大奇;人工神經(jīng)網(wǎng)絡(luò)研究現(xiàn)狀及其展望[J];江南大學學報;2004年01期

6 黃順泉;關(guān)于港口吞吐量預(yù)測方法選擇的探討[J];集裝箱化;2003年07期

7 蘇高利,鄧芳萍;論基于MATLAB語言的BP神經(jīng)網(wǎng)絡(luò)的改進算法[J];科技通報;2003年02期

8 劉燕;陳一梅;;灰色系統(tǒng)模型在內(nèi)河港口吞吐量預(yù)測中的應(yīng)用[J];水運工程;2006年04期

9 齊興敏;尹朝慶;;基于遺傳—神經(jīng)網(wǎng)絡(luò)的沿海港口吞吐量的預(yù)測[J];中國水運(理論版);2006年03期

10 孫永明;鄭光平;;基于灰理論的港口吞吐量預(yù)測研究[J];中國水運(理論版);2007年04期

相關(guān)博士學位論文 前1條

1 寧愛平;人工蜂群算法及其在語音識別中的應(yīng)用研究[D];太原理工大學;2013年

相關(guān)碩士學位論文 前4條

1 王龍;人工蜂群優(yōu)化BP神經(jīng)網(wǎng)絡(luò)在入侵檢測中的應(yīng)用[D];河南大學;2015年

2 向娜;基于神經(jīng)網(wǎng)絡(luò)和人工蜂群算法的水質(zhì)評價和預(yù)測研究[D];華南理工大學;2012年

3 賈磊;欽州港引航服務(wù)發(fā)展研究[D];大連海事大學;2012年

4 石嘯;天津港引航中心戰(zhàn)略發(fā)展規(guī)劃研究[D];天津大學;2009年

，

本文編號：1797357