發(fā)布時間：2018-04-15 17:55

  本文選題：潮汐預(yù)報 + 調(diào)和分析��； 參考：《大連海事大學(xué)》2015年碩士論文

【摘要】：潮汐預(yù)報在海上交通、港口建設(shè)和潮汐能利用等領(lǐng)域都具有重要意義,隨著航運業(yè)的不斷發(fā)展,以及對航行安全和航運效率的要求,對潮汐數(shù)值預(yù)報的精度也提出了更高的要求。將神經(jīng)網(wǎng)絡(luò)應(yīng)用于潮汐預(yù)報領(lǐng)域是近年來出現(xiàn)的一種新的研究方向。反向傳播學(xué)習(xí)(Back Propagation)申經(jīng)網(wǎng)絡(luò)在模式識別和系統(tǒng)預(yù)測領(lǐng)域應(yīng)用廣泛,本文將BP神經(jīng)網(wǎng)絡(luò)用于潮汐預(yù)報,對BP神經(jīng)網(wǎng)絡(luò)在潮汐預(yù)報領(lǐng)域的應(yīng)用進行了探討。傳統(tǒng)調(diào)和分析法進行潮汐預(yù)報時,由于僅考慮了潮汐天文潮部分的影響,導(dǎo)致其在復(fù)雜環(huán)境因素影響下的海區(qū)預(yù)測精度明顯下降。針對傳統(tǒng)調(diào)和分析預(yù)報方法無法實現(xiàn)潮汐非天文潮部分準(zhǔn)確預(yù)報的問題,本文建立了一種使用BP神經(jīng)網(wǎng)絡(luò)直接進行潮汐預(yù)報的模型。該模型基于實測潮汐數(shù)據(jù)進行實時的短期潮汐預(yù)測,提高了短期潮汐預(yù)測精度。模塊化設(shè)計是解決復(fù)雜非線性問題的一種思路,通過分析潮汐的組成成分,提出了一種基于BP神經(jīng)網(wǎng)絡(luò)的模塊化潮汐預(yù)報模型。模型包含了用于預(yù)測潮汐天文潮部分的調(diào)和分析預(yù)測模塊以及用于預(yù)測非天文潮部分的BP神經(jīng)網(wǎng)絡(luò)預(yù)測模塊。模塊化模型有效實現(xiàn)了預(yù)測功能的區(qū)分,將調(diào)和分析預(yù)報法能夠?qū)崿F(xiàn)長期、穩(wěn)定的天文潮預(yù)報的優(yōu)點與BP神經(jīng)網(wǎng)絡(luò)能夠?qū)崿F(xiàn)潮汐非線性及未建模部分預(yù)報的優(yōu)點相結(jié)合。在保證預(yù)測穩(wěn)定性的前提下,進一步提高了預(yù)報的精度。將提出的模塊化預(yù)測模型與傳統(tǒng)調(diào)和分析法、BP神經(jīng)網(wǎng)絡(luò)直接預(yù)測法相比較,并進行了計算機仿真驗證。實驗證明,對于短期潮汐預(yù)報而言,模塊化模型的預(yù)測性能要強于調(diào)和分析法和BP神經(jīng)網(wǎng)絡(luò)直接預(yù)報法。
[Abstract]:Tidal forecasting is of great significance in the fields of maritime traffic, port construction and tidal energy utilization. With the continuous development of the shipping industry, as well as the requirements of navigation safety and efficiency,A higher requirement for the accuracy of numerical tidal prediction is also put forward.The application of neural network to tidal prediction is a new research direction in recent years.Backpropagation Learning back Propagation (BP) network is widely used in pattern recognition and system prediction. In this paper, the application of BP neural network in tidal prediction is discussed.When the traditional harmonic analysis method is used to predict the tide, the accuracy of the sea area prediction under the influence of complex environmental factors is obviously decreased because the influence of the tidal astronomical tide part is only taken into account.Aiming at the problem that the traditional harmonic analysis and prediction method can not realize the accurate prediction of tidal non-astronomical tide, this paper presents a direct tidal prediction model using BP neural network.Based on the measured tidal data, the model can predict the short term tide in real time and improve the accuracy of the short term tide prediction.Modular design is a method to solve complex nonlinear problems. By analyzing the component of tide, a modular tidal prediction model based on BP neural network is proposed.The model includes harmonic analysis and prediction module for predicting tidal astronomical tide and BP neural network for predicting non-astronomical tide.The modular model can effectively distinguish the prediction function, combining the advantages of harmonic analysis forecasting method to achieve long-term and stable astronomical tide prediction, and BP neural network to achieve tidal nonlinear and unmodeled partial prediction.The prediction accuracy is further improved under the premise of ensuring the prediction stability.The proposed modular prediction model is compared with the BP neural network direct prediction method of traditional harmonic analysis method, and computer simulation is carried out to verify it.Experimental results show that the prediction performance of the modular model is better than that of harmonic analysis and BP neural network direct prediction for short term tidal forecasting.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P731.34

【參考文獻】

相關(guān)期刊論文 前2條

1 沈清波;丁元明;;基于模塊化模型的自適應(yīng)預(yù)失真技術(shù)[J];遼寧石油化工大學(xué)學(xué)報;2010年02期

2 唐巖;暴景陽;劉雁春;張立華;;短期潮汐潮流數(shù)據(jù)的正交潮響應(yīng)分析研究[J];武漢大學(xué)學(xué)報(信息科學(xué)版);2010年10期

，

本文編號：1755168