An adaptive hierarchical fuzzy logic system for modelling of financial systems: Research Articles
An adaptive hierarchical fuzzy logic system for modelling of financial systems: Research Articles
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DOI：
10.1002/isaf.v12:1
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
M. Mohammadian;M. Kingham
中科院分区：
文献类型：
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作者：
M. Mohammadian;M. Kingham
本文开发了一种使用遗传算法的智能分层模糊逻辑系统，用于澳大利亚利率的预测和建模。该系统采用分层模糊逻辑系统，其中遗传算法用作学习模糊规则知识库的训练方法，该模糊规则知识库用于澳大利亚利率预测。开发了分层模糊逻辑系统来建模和预测三个月（季度）利率波动。该系统经过进一步训练，可以对六个月和一年的利率进行建模和预测。该系统采用前两个、三个、然后四个、最后五个分层知识库来建模和预测利率。还开发了一种使用模糊逻辑和遗传算法的称为前馈模糊逻辑系统的新颖架构来预测利率。还开发了反向传播分层神经网络系统来预测三个月、六个月和一年期间的利率。然后将这两个系统获得的结果与分层模糊逻辑系统的结果进行比较，得出的结论取决于澳大利亚所有利率预测系统的准确性。版权所有 © 2004 约翰·威利父子有限公司
In this paper an intelligent hierarchical fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates in Australia is developed. The proposed system uses a hierarchical fuzzy logic system in which a genetic algorithm is used as a training method for learning the fuzzy rules knowledge bases that are used for prediction of interest rates in Australia.A hierarchical fuzzy logic system is developed to model and predict three-month (quarterly) interest rate fuctuations. The system is further trained to model and predict interest rates for six-month and one-year periods. The proposed system is developed with frst two, three, then four and fnally fve hierarchical knowledge bases to model and predict interest rates.A novel architecture called a feed forward fuzzy logic system using fuzzy logic and genetic algorithms is also developed to predict interest rates. A back-propagation hierarchical neural network system is also developed to predict interest rates for three-month, six-month and one-year periods. The results obtained from these two systems are then compared with the hierarchical fuzzy logic system results and conclusions are drown on the accuracy of all systems for prediction of interest rates in Australia. Copyright © 2004 John Wiley & Sons, Ltd.