论文标题
改进的在线学习算法，用于一般模糊Min-Max神经网络
An improved online learning algorithm for general fuzzy min-max neural network
论文作者
论文摘要
本文提出了一般模糊的Min-Max神经网络（GFMM）的当前在线学习算法的改进版本，以解决有关扩展和收缩步骤的现有问题，以及处理在决策边界上的看不见数据的方式。这些缺点降低了其分类性能，因此在本研究中提出了改进的算法来解决上述局限性。所提出的方法不将收缩过程用于重叠的输音箱，这更有可能增加文献中所示的错误率。经验结果表明，与原始版本和其他模糊的Min-Max分类器相比，所提出方法的分类准确性和稳定性的提高。为了降低对这种新的在线学习算法的训练样本表现顺序的敏感性，还提出了一种简单的集合方法。
This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries. These drawbacks lower its classification performance, so an improved algorithm is proposed in this study to address the above limitations. The proposed approach does not use the contraction process for overlapping hyperboxes, which is more likely to increase the error rate as shown in the literature. The empirical results indicated the improvement in the classification accuracy and stability of the proposed method compared to the original version and other fuzzy min-max classifiers. In order to reduce the sensitivity to the training samples presentation order of this new on-line learning algorithm, a simple ensemble method is also proposed.
