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BeeRBF: A bee-inspired data clustering approach to design RBF neural network classifiers

机译:BeeRBF:一种受蜜蜂启发的数据聚类方法,用于设计RBF神经网络分类器

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摘要

Different methods have been used to train radial basis function (RBF) neural networks. This paper proposes the use of a bee-inspired algorithm, named cOptBees, plus a heuristic to automatically select the number, location and dispersions of basis functions to be used in RBF networks. cOptBees was originally designed to solve data clustering problems and the prototypes determined by the algorithm will be selected as the centers for the RBF network. The presented approach, named BeeRBF, is used to solve classification problems and is evaluated both in terms of the decision boundaries generated and classification accuracy. The performance of BeeRBF was compared with that of k-means, random center selection and some other proposals from the literature. The results show that BeeRBF is competitive and has the advantage of automatically determining the number of centers to be used in the RBF network. (C) 2015 Elsevier BAT. All rights reserved.
机译:已经使用了不同的方法来训练径向基函数(RBF)神经网络。本文提出了一种由蜜蜂启发的算法cOptBees的使用,以及一种启发式算法来自动选择要在RBF网络中使用的基本函数的数量,位置和离散度。 cOptBees最初设计用于解决数据聚类问题,由算法确定的原型将被选作RBF网络的中心。所提出的名为BeeRBF的方法用于解决分类问题,并根据生成的决策边界和分类准确性进行评估。将BeeRBF的性能与k均值,随机中心选择以及文献中的其他一些建议进行了比较。结果表明,BeeRBF具有竞争力,并具有自动确定RBF网络中使用的中心数量的优势。 (C)2015年Elsevier BAT。版权所有。

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