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随着理论与应用的需要,对模糊时间序列模型的研究和应用越来越深入。提出从论域的划分和模糊规则的提取两个方面对传统模型进行改进。模型首先采用自动聚类的方法对论域进行划分,并在此基础上建立具有权重的模糊规则;然后,利用粒子群算法对模型进行优化,进一步提高预测精度;最后,将Alabama大学入学人数作为本模型的实验数据。实验结果表明该模型是可行的,其预测结果明显优于参照预测模型。
With the need of theory and application, the research and application of fuzzy time series model is more and more in-depth. It proposes that the traditional model should be improved from two aspects: the division of the universe of discourse and the extraction of fuzzy rules. At first, the model divides the universe of discourse by using automatic clustering method and establishes fuzzy rules with weights based on it. Then, the particle swarm optimization is used to optimize the model to further improve the prediction accuracy. Finally, the number of students in Alabama University is taken as Experimental data of this model. Experimental results show that the model is feasible and its prediction result is obviously better than the reference prediction model.