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.
The author gratefully acknowledges that this work has been supported by the 2006 K.C. Wong Education Foundation.
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Fan, J., Shou, L., Dong, J. (2006). Improving the Intelligent Prediction Model for Macro-economy. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_16
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DOI: https://doi.org/10.1007/11816157_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
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