• DocumentCode
    3013245
  • Title

    Application Research of Rough-GA-BP Method in the Real Estate Early-Warning System

  • Author

    Dongmei, Han

  • Author_Institution
    Sch. of Inf. Manage. & Eng., Shanghai Univ. of Finance & Econ., SHUFE, Shanghai, China
  • fYear
    2010
  • fDate
    25-27 June 2010
  • Firstpage
    483
  • Lastpage
    485
  • Abstract
    Early-warning system of China´s real estate is still in the development of a sound stage, and there are following two main aspects. Firstly, the selection of indicators is to be improved. Secondly, predictive capability of the turning point about the real estate business cycle is to be improved. Based on the above-mentioned problems, the Rough-GA-BP model proposed is applied to the real estate early-warning system. Based on Rough-GA-BP model, the prediction is divided into the following steps. First, based on the theory of Rough-Ann we can make indicators screening. Second, the genetic algorithm (GA) is applied to BP neural network, and according to the GA-BP model, we have optimized its hidden layer structures and the initial right. Third, make a good use of Rough-GA-BP model constructed to predict the turning point of the real estate business cycle. Finally, compare the results predicted based on the Rough-GA-BP model with the ones based on the traditional methods.
  • Keywords
    backpropagation; economic indicators; genetic algorithms; prediction theory; property market; rough set theory; BP neural network; China real estate; Rough-Ann theory; genetic algorithm; predictive capability; real estate business cycle; real estate early-warning system; rough-GA-BP method; Alarm systems; Artificial neural networks; Biological system modeling; Indexes; Investments; Predictive models; Turning; Early-warning; Genetic algorithm; Neural network; Turning point;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Control Engineering (ICECE), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-6880-5
  • Type

    conf

  • DOI
    10.1109/iCECE.2010.124
  • Filename
    5631570