Improving the Intelligent Prediction Model for Macro-economy

  • Jianbo Fan
  • Lidan Shou
  • Jinxiang Dong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


This paper presents a novel approach to macro-economy forecasting based on the Fuzzy Neural Networks. This method employs the expert opinions, statistical analysis and the Genetic Algorithm, to enhance the model of Fuzzy Neural Network. Our method combines the expert opinions and the results of statistical analysis to determine the input parameters of the prediction model, and adopts the Genetic Algorithm to process the original sample data. We use the fuzzy logic system to establish a set of fuzzy rules and utilize an EBP (Error Back Propagation) algorithm to train the network and adjust the parameters of the membership functions. The experimental results of the system indicates that the method is efficient and robust, producing high-precision results. This method could be extended to other application areas.


Membership Function Fuzzy Rule Fuzzy Neural Network Fuzzy Subset Fuzzy Logic System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jianbo Fan
    • 1
  • Lidan Shou
    • 2
  • Jinxiang Dong
    • 2
  1. 1.College of Electron & Information EngineeringNingbo University of TechnologyNingbo
  2. 2.Institute of Artificial IntelligenceZhejiang UniversityHangzhou

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