基于人工神经网络的模糊宗地地价评估模型研究  

An Improved Land Price Evaluation Method Based on Artificial Neural Network and Fuzzy Mathematics

作　　者：王华 李雯雯 牛继强 刘殿峰 WANG Hua;LI Wenwen;NIU Jiqiang;LIU Dianfeng(College of Computer and Communication Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002,China;College of Geographic Sciences,Xinyang Normal University,Xinyang 464000,China;College of Resource and Environment Science,Wuhan University,Wuhan 430079,China)

机构地区：[1]郑州轻工业大学计算机与通信工程学院,河南郑州450002 [2]信阳师范学院地理科学学院,河南信阳464000 [3]武汉大学资源与环境学院,湖北武汉430079

出　　处：《信阳师范学院学报（自然科学版）》2020年第1期76-82,共7页Journal of Xinyang Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(41601418,41771438);河南省科技攻关项目(172102210539);数字制图与国土信息应用工程国家测绘地理信息局重点实验室开放研究基金资助项目

摘　　要：针对传统市场比较法中可比实例选取较随意且特征权重确定较主观等问题,引入了基于人工神经网络的地价影响特征权重学习机制.以地价影响特征向量作为输入空间,土地价格作为输出空间,通过神经网络的反馈学习机制不断调整神经元之间的连接权重,建立地价与多维特征之间的精准复杂映射关系,并基于网络参数提取输入特征权重系数,然后耦合模糊数学方法选择比较案例,通过比较案例的加权价格最终计算得到评估对象价格.以湖北省武汉市某宗地评估为实例,结果表明:改进后的市场比较法的估价准确度要比现行土地估价市场比较法平均高出3.69%.Traditional market comparison approach has some problems such as the arbitrariness of trade cases selection and the subjectivity of the weight definition of land price characteristics. A new mechanism of feature weight learning based on Back-Propagation neural network(BPNN) was adopted to solve the above problems. A group of 17-dimensional original features reflecting status of land location and quality were employed as input, and the land prices were used as output of BPNN model. The model could build the complex function relationship fully between the affecting factors and land prices by regulating variable weight connection based on the feedback mechanisms of neural network. The weight coefficient of land price characteristics could be calculated by using the structure parameters of BP network, and has been used in the computing model of similarity degree based on the fuzzy mathematics theory. Then the comparative cases were selected in accordance with the similarity degree. Weighted land price of each selected comparative case was also determined based on the similarity degree. Finally took the Wuhan city in Hubei province as a case study, feasibility and validity of the improvement were validated. The results of the present study indicated that the improved model got better assessment accuracy than the present method with an increase of 3.69 percent.

关 键 词：宗地地价评估 BP神经网络 模糊数学 贴近度 特征权重 

分 类 号：F301.2[经济管理—产业经济]