本文選題：旅游需求 + 神經(jīng)網(wǎng)絡(luò)��； 參考：《西北農(nóng)林科技大學(xué)》2013年碩士論文

【摘要】：旅游市場(chǎng)需求預(yù)測(cè)常見(jiàn)的方法有：時(shí)間序列預(yù)測(cè)法和因果模型預(yù)測(cè)法。然而旅游市場(chǎng)往往受到許多因素的影響，這些因素之間關(guān)系錯(cuò)綜復(fù)雜，而且有很多不可預(yù)知的因素，常見(jiàn)的方法難以得到可靠的預(yù)測(cè)結(jié)果。 人工神經(jīng)網(wǎng)絡(luò)是模擬大腦的神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)和功能的數(shù)學(xué)模型，它是由大量處理單元組成的自適應(yīng)動(dòng)態(tài)系統(tǒng)，用來(lái)處理數(shù)據(jù)樣本中的非線性關(guān)系，具有良好的自適應(yīng)性、自組織及很強(qiáng)的學(xué)習(xí)、容錯(cuò)和抗干擾能力，可以對(duì)復(fù)雜系統(tǒng)進(jìn)行靈活方便的建模，使得它在實(shí)際應(yīng)用中成為一種很好的分類和預(yù)測(cè)工具。一般來(lái)說(shuō)，旅游市場(chǎng)是受到許多不可預(yù)知因素的影響，所以在進(jìn)行旅游市場(chǎng)需求預(yù)測(cè)時(shí)用神經(jīng)網(wǎng)絡(luò)模型分析是比較優(yōu)越的。本文分析、研究了人工神經(jīng)網(wǎng)絡(luò)的基本理論、預(yù)測(cè)方法，對(duì)預(yù)測(cè)指標(biāo)、預(yù)測(cè)模型的選擇、建模流程和方法進(jìn)行了初步探討。 本文用BP神經(jīng)網(wǎng)絡(luò)模型對(duì)太白山自然保護(hù)區(qū)生態(tài)旅游需求進(jìn)行了預(yù)測(cè)分析。結(jié)果證實(shí)了該模型的有效性。在太白山自然保護(hù)區(qū)生態(tài)旅游需求預(yù)測(cè)中，采用2-8-1神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)，預(yù)測(cè)了太白山自然保護(hù)區(qū)2011～2020年旅游人數(shù)，，將實(shí)驗(yàn)數(shù)據(jù)與實(shí)際數(shù)據(jù)作對(duì)比，表明該模型有良好預(yù)測(cè)能力，網(wǎng)絡(luò)泛化能力良好。
[Abstract]:The common methods of tourism market demand prediction are: time series forecasting and causality model forecasting. However, the tourism market is often affected by many factors, these factors are intricate, and there are many unpredictable factors, common methods are difficult to obtain reliable prediction results. Artificial neural network is a mathematical model that simulates the neural network structure and function of the brain. It is an adaptive dynamic system composed of a large number of processing units, which is used to deal with nonlinear relationships in data samples and has good adaptability. Self-organization and strong learning, fault-tolerant and anti-interference ability, can be used for flexible and convenient modeling of complex systems, which makes it a good tool for classification and prediction in practical applications. Generally speaking, the tourism market is affected by many unpredictable factors, so it is better to use neural network model to forecast the demand of tourism market. In this paper, the basic theory and prediction method of artificial neural network are analyzed and studied. The prediction index, the selection of prediction model, the modeling process and method are discussed preliminarily. In this paper, the demand for ecotourism in Taibai Mountain Nature Reserve is predicted by BP neural network model. The results show that the model is effective. In the prediction of ecotourism demand in Taibai Mountain Nature Reserve, 2-8-1 neural network structure was used to predict the tourism population in Taibai Mountain Nature Reserve from 2011 to 2020. The comparison between experimental data and actual data shows that the model has good predictive ability. Network generalization ability is good.
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F592.7;TP183

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