FUZZY VERSIONS OF KOHONENS NET AND MLP-BASED CLASSIFICATION - PERFORMANCE EVALUATION FOR CERTAIN NONCONVEX DECISION REGIONS
FUZZY VERSIONS OF KOHONENS NET AND MLP-BASED CLASSIFICATION - PERFORMANCE EVALUATION FOR CERTAIN NONCONVEX DECISION REGIONS
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DOI：
10.1016/0020-0255(94)90014-0
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发表时间：
1994-01-01
影响因子：
8.1
通讯作者：
MITRA, S
中科院分区：
文献类型：
--
作者：
PAL, SK;MITRA, S
具有非凸决策区域的某些线性不可分离模式类的分类是一个无法由正态分布的贝叶斯分类器或其他基于度量的方法有效处理的问题。试图在这里证明模糊版本的Kohonen的网络和多层感知器的这种模式的分类能力。在这些模型中，输入描述和输出决策中涉及的不确定性已经被模糊集的概念所照顾，而神经网络理论有助于生成所需的凹和/或断开的决策区域。这些模糊模型（在各自的传统版本，贝叶斯分类器和其他七个现有的神经算法）的优先级已充分建立，当它们被实现在不同的线性不可分离的模式类。在输入的模糊化的效果进行了研究，这两个模型。还研究了模式类的先验概率在权值更新的反向传播过程中的贡献。
Classification of certain linearly nonseparable pattern classes with nonconvex decision regions is a problem that cannot be efficiently handled by the Bayes' classifier for normal distributions or other metric-based methods. An attempt is made here to demonstrate the ability of fuzzy versions of Kohonen's net and the multilayer perceptron for classification of such patterns. In these models, the uncertainties involved in the input description and output decision have been taken care of by the concept of fuzzy sets whereas the neural net theory helps to generate the required concave and/or disconnected decision regions. Superiority of these fuzzy models (over the respective conventional versions, the Bayes' classifier and seven other existing neural algorithms) has been adequately established when they are implemented on different sets of linearly nonseparable pattern classes. The effect of fuzzification at the input has been investigated for both models. The contribution of the a priori probabilities of the pattern classes in the back-propagation procedure for weight updating has also been studied.