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首页> 外文期刊>Journal of software >Applying Unascertained Theory, Principal Component Analysis and ACO-based Artificial Neural Networks for Real Estate Price Determination
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Applying Unascertained Theory, Principal Component Analysis and ACO-based Artificial Neural Networks for Real Estate Price Determination

机译：应用不确定性理论，主成分分析和基于ACO的人工神经网络确定房地产价格

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摘要

Real estate industry is both capital-intensive, highly related industries and industries essential to provide the daily necessities. However, the real estate pricing models and methods of research rarely receive the critical attention and development it deserves. In this paper, we present a multi-resolution approach for the determination of the real estate pricing. The proposed method firstly utilizes unascertained theory to describe and quantity the price indices of the real estate, then principal component analysis (PCA) were introduced in to eliminate the real estate pricing indices having the relativities and overlap information. The representative indices from principal component analysis process substitute for the primary indexes. Thus subjective random problem in choosing indices can be avoided. Finally, Using ACO-based artificial neural networks, real estate pricing was analyzed and the results show that this method is more convenient and practical compared with the traditional one.
机译：房地产行业既是资本密集型，高度相关的行业，又是提供日常必需品的行业。但是，房地产定价模型和研究方法很少得到应有的重视和发展。在本文中，我们提出了一种用于房地产价格确定的多分辨率方法。该方法首先利用不确定性理论对房地产价格指数进行描述和定量，然后引入主成分分析法（PCA），以消除具有相对性和重叠信息的房地产价格指数。主成分分析过程中的代表性指标替代了主要指标。因此可以避免选择指标时的主观随机问题。最后，利用基于ACO的人工神经网络对房地产价格进行了分析，结果表明该方法比传统方法更加方便实用。

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