本文選題：潮汐預(yù)報(bào) + 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(bào)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(yùn)業(yè)的不斷發(fā)展,以及對(duì)航行安全和航運(yùn)效率的要求,對(duì)潮汐數(shù)值預(yù)報(bào)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(bào)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(shí)別和系統(tǒng)預(yù)測(cè)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(bào),對(duì)BP神經(jīng)網(wǎng)絡(luò)在潮汐預(yù)報(bào)領(lǐng)域的應(yīng)用進(jìn)行了探討。傳統(tǒng)調(diào)和分析法進(jìn)行潮汐預(yù)報(bào)時(shí),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(cè)精度明顯下降。針對(duì)傳統(tǒng)調(diào)和分析預(yù)報(bào)方法無法實(shí)現(xiàn)潮汐非天文潮部分準(zhǔn)確預(yù)報(bào)的問題,本文建立了一種使用BP神經(jīng)網(wǎng)絡(luò)直接進(jìn)行潮汐預(yù)報(bào)的模型。該模型基于實(shí)測(cè)潮汐數(shù)據(jù)進(jìn)行實(shí)時(shí)的短期潮汐預(yù)測(cè),提高了短期潮汐預(yù)測(cè)精度。模塊化設(shè)計(jì)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(bào)模型。模型包含了用于預(yù)測(cè)潮汐天文潮部分的調(diào)和分析預(yù)測(cè)模塊以及用于預(yù)測(cè)非天文潮部分的BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模塊。模塊化模型有效實(shí)現(xiàn)了預(yù)測(cè)功能的區(qū)分,將調(diào)和分析預(yù)報(bào)法能夠?qū)崿F(xiàn)長(zhǎng)期、穩(wěn)定的天文潮預(yù)報(bào)的優(yōu)點(diǎn)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(bào)的優(yōu)點(diǎn)相結(jié)合。在保證預(yù)測(cè)穩(wěn)定性的前提下,進(jìn)一步提高了預(yù)報(bào)的精度。將提出的模塊化預(yù)測(cè)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(cè)法相比較,并進(jìn)行了計(jì)算機(jī)仿真驗(yàn)證。實(shí)驗(yàn)證明,對(duì)于短期潮汐預(yù)報(bào)而言,模塊化模型的預(yù)測(cè)性能要強(qiáng)于調(diào)和分析法和BP神經(jīng)網(wǎng)絡(luò)直接預(yù)報(bào)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(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：P731.34

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本文編號(hào)：1755168