人工智能方法在估价领域的研究与应用
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摘要
随着计算机科学与技术的高速发展,人工智能技术已渗透到各学科领域和各行各业,人工智能的理论、方法与应用研究是当前非常热点的课题。本文就人工智能方法在估价领域的应用进行系统研究,研究其在土地估价、房地产估价、工程估价等估价领域的综合应用。
一方面,本文系统阐述了土地估价、房地产估价、工程估价的理论基础,剖析了BP(Back Propagation)神经网络及其改进原理、径向基函数(Radical Basis Function,简称RBF)神经网络和案例推理的理论基础及基于人工神经网络的专家系统设计原理。选用Surfer和ArcGIS这两种地理信息处理软件分别建立数字地价模型,进行可视化地价分析与管理;选用SPSS统计分析软件,根据各个变量之间的相关性分析确定工程造价的主要影响因素。
另一方面,采用调查研究与实际检验,理论与实践相结合的的研究方法,系统分析并构建科学合理的人工智能方法在估价领域中的应用模型。以厦门市土地价格评估为例,建立了基于BP神经网络的土地估价模型;以厦门市住宅用地样点地价为例,建立了基于Surfer和ArcGIS的数字地价模型;以厦门市住宅房地产价格评估为例,建立了基于改进BP神经网络的房地产估价模型。此外,将厦门市55个工程造价指标汇编成案例库,系统阐述了基于RBF神经网络的工程估价模型的建立过程,基于案例推理的工程估价模型的建立过程和基于RBF神经网络的工程估价专家系统的建立过程。
构建的基于人工神经网络的专家系统,利用具有极强自学习能力和能够高效应对网络参数之间的高度非线性关系的RBF神经网络,解决了案例推理方法应用于估价领域时案例之间的相似性度量难以确定的问题。采用案例推理的方法对神经网络的推理结果作出合理修正和解释,解决了神经网络的黑箱决策过程,得到了可以接受的结果。
研究结果表明,神经网络、案例推理、基于人工神经网络的专家系统等人工智能方法可以有效地应用于土地估价、房地产估价和工程估价等估价领域,不仅具有重要的理论意义,而且具有较高的实用价值。
With the rapid development of computer science and technology, artificial intelligence technology is used widely in various fields. Nowadays, the issues focusing on the theory, method and applications of artificial intelligence become more and more common. In this case, the applications of artificial intelligence methods in the field of valuation, such as the land valuation, the real estate appraisal and the project appraisal, are studied in this paper.
On one hand, the land valuation, real estate valuation and engineering valuation theory are described in detail. The basic theory of Back Propagation (BP) neural network and its improvement, the basic theory of Radical Basis Function (RBF) neural network, the basic theory of case-based reasoning and the design principles of expert system based on artificial neural network are analyzed. The digital land price model, which is used as the analysis and management of land prices visualization, is set up both with the geographical information processing software Surfer and the ArcGIS. The main influence factors of project cost are determined according to the analysis of the relevance between every variable with SPSS.
On the other hand, some scientific and rational models about the application of artificial intelligence in the field of valuation is set up by combining research with practical test and joining theoretical analysis with practice proven. These models are: the model of land valuation based on BP neural network by taking the assessment of land prices in Xiamen City for example, the digital land price model based on Surfer and ArgGIS by the example of Xiamen residential land and the model of the estimation of real estate based on improved BP nerual network by taking the residential real estate appraisal of Xiamen for example. In addition, with 55 indexes of project assessment compiled in the case library from Xiamen, the building processes of evaluation model based on RBF neural network, based on case-based reasoning and the expert system of project cost based on the RBF neural network are elaborated .
The expert systems based on RBF neural network with efficient learning ability, can fit the strong nonlinear relationship between network parameters, and solve the problem which is difficult to determine the analogies between the cases used in the field of valuation. The result is acceptable, which is amended and interpreted to the nerve network by case-based reasoning, and also solve the decision-making process which is called black box.
The results show that the artificial intelligence methods such as neural networks, case-based reasoning, and artificial neural network based expert systems, can be applied into land valuation, real estate appraisal, valuation of engineering and other areas of valuation, which is not only has great theoretical significance, but also has a significant practical value.
一方面,本文系统阐述了土地估价、房地产估价、工程估价的理论基础,剖析了BP(Back Propagation)神经网络及其改进原理、径向基函数(Radical Basis Function,简称RBF)神经网络和案例推理的理论基础及基于人工神经网络的专家系统设计原理。选用Surfer和ArcGIS这两种地理信息处理软件分别建立数字地价模型,进行可视化地价分析与管理;选用SPSS统计分析软件,根据各个变量之间的相关性分析确定工程造价的主要影响因素。
另一方面,采用调查研究与实际检验,理论与实践相结合的的研究方法,系统分析并构建科学合理的人工智能方法在估价领域中的应用模型。以厦门市土地价格评估为例,建立了基于BP神经网络的土地估价模型;以厦门市住宅用地样点地价为例,建立了基于Surfer和ArcGIS的数字地价模型;以厦门市住宅房地产价格评估为例,建立了基于改进BP神经网络的房地产估价模型。此外,将厦门市55个工程造价指标汇编成案例库,系统阐述了基于RBF神经网络的工程估价模型的建立过程,基于案例推理的工程估价模型的建立过程和基于RBF神经网络的工程估价专家系统的建立过程。
构建的基于人工神经网络的专家系统,利用具有极强自学习能力和能够高效应对网络参数之间的高度非线性关系的RBF神经网络,解决了案例推理方法应用于估价领域时案例之间的相似性度量难以确定的问题。采用案例推理的方法对神经网络的推理结果作出合理修正和解释,解决了神经网络的黑箱决策过程,得到了可以接受的结果。
研究结果表明,神经网络、案例推理、基于人工神经网络的专家系统等人工智能方法可以有效地应用于土地估价、房地产估价和工程估价等估价领域,不仅具有重要的理论意义,而且具有较高的实用价值。
With the rapid development of computer science and technology, artificial intelligence technology is used widely in various fields. Nowadays, the issues focusing on the theory, method and applications of artificial intelligence become more and more common. In this case, the applications of artificial intelligence methods in the field of valuation, such as the land valuation, the real estate appraisal and the project appraisal, are studied in this paper.
On one hand, the land valuation, real estate valuation and engineering valuation theory are described in detail. The basic theory of Back Propagation (BP) neural network and its improvement, the basic theory of Radical Basis Function (RBF) neural network, the basic theory of case-based reasoning and the design principles of expert system based on artificial neural network are analyzed. The digital land price model, which is used as the analysis and management of land prices visualization, is set up both with the geographical information processing software Surfer and the ArcGIS. The main influence factors of project cost are determined according to the analysis of the relevance between every variable with SPSS.
On the other hand, some scientific and rational models about the application of artificial intelligence in the field of valuation is set up by combining research with practical test and joining theoretical analysis with practice proven. These models are: the model of land valuation based on BP neural network by taking the assessment of land prices in Xiamen City for example, the digital land price model based on Surfer and ArgGIS by the example of Xiamen residential land and the model of the estimation of real estate based on improved BP nerual network by taking the residential real estate appraisal of Xiamen for example. In addition, with 55 indexes of project assessment compiled in the case library from Xiamen, the building processes of evaluation model based on RBF neural network, based on case-based reasoning and the expert system of project cost based on the RBF neural network are elaborated .
The expert systems based on RBF neural network with efficient learning ability, can fit the strong nonlinear relationship between network parameters, and solve the problem which is difficult to determine the analogies between the cases used in the field of valuation. The result is acceptable, which is amended and interpreted to the nerve network by case-based reasoning, and also solve the decision-making process which is called black box.
The results show that the artificial intelligence methods such as neural networks, case-based reasoning, and artificial neural network based expert systems, can be applied into land valuation, real estate appraisal, valuation of engineering and other areas of valuation, which is not only has great theoretical significance, but also has a significant practical value.
引文
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