TY -的A2 -杨,君非盟- Yasnitsky,列昂尼德•n . AU - Yasnitsky维塔利·l . AU - Alekseev亚历山大·o . PY - 2021 DA - 2021/03/03 TI -质量鉴定与场景的复杂的神经网络模型预测城市房地产市场价值,适应本身的空间和时间SP - 5392170六世- 2021 AB -在现代科学文献中,有许多报道关于神经网络技术的成功应用为解决复杂的应用问题,特别是建模城市房地产市场。有神经网络模型能够进行质量评估房地产对象考虑它们的构造和操作特征。然而,这些模型是静态的,因为他们不考虑随时间变化的经济形势。因此,他们很快就会过时,需要频繁的更新。此外,如果他们被设计为一个特定的城市,不适合其他城市。另一方面,有几个动态模型考虑到整体的经济状况,旨在预测和研究房地产市场的整体价格状况。这种动态模型并不适合大规模房地产评估。本文的目的是开发一个方法和创建一个复杂的模型,静态和动态的属性模型。此外,我们应该适合评估房地产综合模型在许多城市。这一目标实现自我们的模型是基于神经网络训练例子考虑建设和操作特征,以及地理环境特点,以及图示宏观经济参数,描述一个特定地区的经济状况,国家,和世界。 A set of examples for training and testing the neural network were formed on the basis of statistical data of real estate markets in a number of Russian cities for the period from 2006 to 2020. Thus, many examples included the data relating to the periods of the economic calm for Russia, along with the periods of crisis, recovery, and growth of the Russian and global economy. Due to this, the model remains relevant with the changes of the international economic situation and it takes into account the specifics of regions. The model proved to be suitable for solving the following tasks: industrial economic analysis, company strategic and operational management, analytical and consulting support of investment, and construction activities of professional market participants. The model can also be used by government agencies authorized to conduct public cadastral assessment for calculating property taxes. SN - 1076-2787 UR - https://doi.org/10.1155/2021/5392170 DO - 10.1155/2021/5392170 JF - Complexity PB - Hindawi KW - ER -