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A new method for weighted fuzzy interpolative reasoning based on PSO-based weights-learning techniques

机译:基于PSO的权重学习技术的加权模糊插值推理新方法

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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.
机译:在本文中,我们提出了一种基于提出的基于PSO的权重学习算法的加权模糊插值推理方法。我们还将应用所提出的方法来处理计算机活动预测问题。实验结果表明,所提出的基于PSO的权重学习算法获得的最优学习权重的加权模糊插值推理方法比现有方法具有较小的相对平方误差率。

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