A new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques
A new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques
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DOI：
10.1109/icsmc.2011.6084021
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
Shyi-Ming Chen;Gandhi Maruli Tua Manalu;Shu-Chuan Shih;T. Sheu;Hsiang-Chuan Liu
中科院分区：
文献类型：
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作者：
Shyi-Ming Chen;Gandhi Maruli Tua Manalu;Shu-Chuan Shih;T. Sheu;Hsiang-Chuan Liu
提出了一种基于双因素高阶模糊趋势逻辑关系群和粒子群优化技术的模糊预测新方法。对主次因子的历史训练数据分别进行模糊化处理，形成双因子高阶模糊逻辑关系。然后，将双因素高阶模糊逻辑关系分组为双因素高阶模糊趋势逻辑关系组。最后，利用粒子群优化技术进行预测，得到每个模糊趋势逻辑关系组的最优权向量。实验结果表明，该方法比现有方法具有更高的平均预测准确率。
This paper presents a new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. We fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors high-order fuzzy logical relationships. Then, we group the two-factors high-order fuzzy logical relationships into two-factors high-order fuzzy-trend logical relationship groups. Finally, we obtain the optimal weighting vectors for each fuzzy-trend logical relationship group by using particle swarm optimization techniques to perform the forecasting. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.