Improving the Intelligent Prediction Model for Macro-economy
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.
KeywordsMembership Function Fuzzy Rule Fuzzy Neural Network Fuzzy Subset Fuzzy Logic System
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- 3.Song, W.G., Yuan, K.: Haptic Modeling for Liver Cutting Based on Fuzzy Neural Network. In: Proceedings IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 1216–1220 (2005)Google Scholar
- 5.Xiong, Z.B., Li, R.J.: Credit Risk Evaluation with Fuzzy Neural Networks on Listed Corporations of China. In: Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, May 28-30, pp. 397–402 (2005)Google Scholar
- 6.Fan, J.B., Yang, J.G.: The Research of Intelligent Predict Model of Macro-economy. In: Neural Network and Computational Intelligence, pp. 371–377. Zhejiang University Press, Hangzhou (2002)Google Scholar
- 7.Wessels, W.J.: Economics. Publisher by Barron’s Educational Series (2000)Google Scholar
- 8.Ningbo Statistic bureau: Ningbo Reform Opening Twenty Years, Vol.1, Vol. 2. Ningbo Press (1998)Google Scholar
- 9.Ningbo Statistic Bureau: Ningbo Statistical Communique (2006), URL: http://www.nbnet.com.cn/homepage/njsj/njsj.php