Improving the Intelligent Prediction Model for Macro-economy
Identifieur interne : 006107 ( Main/Merge ); précédent : 006106; suivant : 006108Improving the Intelligent Prediction Model for Macro-economy
Auteurs : Jianbo Fan ; Lidan Shou [République populaire de Chine] ; Jinxiang Dong [République populaire de Chine]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: 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.
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DOI: 10.1007/11816157_16
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