本文選題：房地產(chǎn)投資 + 粒子群算法��； 參考：《河北工程大學(xué)》2013年碩士論文

【摘要】：房地產(chǎn)開發(fā)是一項高投入、高收益、高風險的投資活動，在開發(fā)過程中會受到社會、政治、經(jīng)濟、技術(shù)等很多不確定因素的影響，在當前我國房地產(chǎn)業(yè)宏觀調(diào)控力度加大的形勢下，投資者如果僅僅依靠經(jīng)驗進行投資，則會具有較大的盲目性，必然會加大房地產(chǎn)投資風險。因而，科學(xué)合理的房地產(chǎn)投資風險評價系統(tǒng)的研究迫在眉睫，運用科學(xué)方法對房地產(chǎn)投資風險進行評價，在理論和實踐方面都具有重要的價值。 本文首先，對房地產(chǎn)投資風險的相關(guān)理論進行了歸納和總結(jié)，并從房地產(chǎn)投資的各個階段出發(fā)，對房地產(chǎn)投資風險因素進行了具體分析，建立了房地產(chǎn)投資風險評價指標體系；其次，闡述了基于改進PSO優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的相關(guān)理論；最后，建立了改進PSO-BP的房地產(chǎn)投資風險評價模型，通過訓(xùn)練與檢測驗證了改進PSO-BP模型的評價效果優(yōu)于一般傳統(tǒng)方法，并將該模型應(yīng)用到房地產(chǎn)投資風險評價的實際案例中，得出了有效的評價結(jié)果。 本文針對BP神經(jīng)網(wǎng)絡(luò)的缺陷，利用改進PSO優(yōu)化BP神經(jīng)網(wǎng)絡(luò)，，實現(xiàn)了BP神經(jīng)網(wǎng)絡(luò)僅在已接近最優(yōu)解的基礎(chǔ)上進行參數(shù)尋優(yōu)的目的，有效的提高了網(wǎng)絡(luò)的尋優(yōu)精度和速度，使房地產(chǎn)投資風險評價效果更好。同時結(jié)合天津市房地產(chǎn)投資的實際案例，重點探討了改進PSO-BP神經(jīng)網(wǎng)絡(luò)在房地產(chǎn)投資風險評價中的應(yīng)用，對提高我國的房地產(chǎn)投資企業(yè)以及相關(guān)決策部門的決策水平具有現(xiàn)實意義。
[Abstract]:Real estate development is an investment activity with high investment, high income and high risk, which will be influenced by many uncertain factors, such as society, politics, economy, technology and so on.Under the situation of increasing macro-control of real estate industry in our country, if investors only rely on experience to invest, it will be blind and will inevitably increase the risk of real estate investment.Therefore, the research of scientific and reasonable real estate investment risk evaluation system is urgent. It is of great value in theory and practice to use scientific methods to evaluate real estate investment risk.First of all, this paper summarizes the theory of real estate investment risk, and analyzes the risk factors of real estate investment from each stage of real estate investment, and establishes the evaluation index system of real estate investment risk.Secondly, the related theory of BP neural network optimization based on improved PSO is expounded. Finally, the risk evaluation model of real estate investment based on improved PSO-BP is established, and the evaluation effect of improved PSO-BP model is proved to be better than that of traditional methods through training and testing.The model is applied to the real estate investment risk evaluation, and the effective evaluation results are obtained.Aiming at the defects of BP neural network, this paper optimizes BP neural network by using improved PSO, realizes the goal of parameter optimization only on the basis of approaching the optimal solution, and effectively improves the precision and speed of optimization.So that real estate investment risk evaluation effect is better.At the same time, the application of improved PSO-BP neural network in real estate investment risk evaluation is discussed in the light of the real estate investment cases in Tianjin.It is of practical significance to improve the level of decision-making of real estate investment enterprises and relevant decision-making departments in our country.
【學(xué)位授予單位】：河北工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP18;F299.23

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