基于pso的权重学习技术的加权模糊插值推理新方法

Shyi-Ming Chen, Wen-Chyuan Hsin, Yu-Chuan Chang
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引用次数: 1

摘要

本文在提出的基于pso的权重学习算法的基础上提出了一种加权模糊插值推理方法。我们还将该方法应用于计算机活动预测问题。实验结果表明,利用基于pso的权重学习算法获得的最优学习权值,所提出的加权模糊插值推理方法比现有方法的相对平方错误率更小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new method for weighted fuzzy interpolative reasoning based on PSO-based weights-learning techniques
In this paper, we present a weighted fuzzy interpolative reasoning method based on the proposed PSO-based weights-learning algorithm. We also apply the proposed method to deal with the computer activity prediction problem. The experimental results show that the proposed weighted fuzzy interpolative reasoning method using the optimally learned weights obtained by the proposed PSO-based weights-learning algorithm gets smaller relative squared error rates than the existing methods.
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