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首页> 外文期刊>Expert systems with applications >Evaluating Classification Performances Of Single-layer Perceptron With A Choquet Fuzzy Integral-based Neuron
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Evaluating Classification Performances Of Single-layer Perceptron With A Choquet Fuzzy Integral-based Neuron

机译：基于Choquet模糊积分神经元的单层感知器分类性能评估

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摘要

The sigmoid function is usually used as the activation function for a well-known classification method,namely the single-layer perceptron.In the function,a weighted sum,in which the additivity among individual variables is assumed,is performed.However,it is known that an assumption of additivity may not be reasonable,since the input variables are not always independent of each other.This paper thus employs a Choquet fuzzy integral-based neuron as an output neuron of the single-layer perceptron.Moreover,the connection weights can be interpreted as fuzzy measure values or degrees of importance of the respective attributes.The connection weights are determined by the genetic algorithms in which the maximization of the training classification performance and the minimization of the errors between the actual and desired outputs of individual training patterns are taken into account.The experimental results further demonstrate that the classification results of the single-layer perceptron with a Choquet fuzzy integral-based neuron are comparable to those of the traditional single-layer perceptron and the other fuzzy classification methods.
机译：sigmoid函数通常用作一种著名的分类方法的激活函数，即单层感知器。在该函数中，执行一个加权和，其中假定各个变量之间的可加性。众所周知，由于输入变量并不总是相互独立的，所以可加性的假设可能并不合理。因此，本文采用基于Choquet模糊积分的神经元作为单层感知器的输出神经元。此外，连接权重权重是由遗传算法确定的，其中遗传分类算法的最大化是训练分类性能的最大化，个体训练模式的实际输出与期望输出之间的误差的最小化是遗传算法所确定的。实验结果进一步证明了单层感知器wi的分类结果基于Choquet模糊积分的神经元可与传统的单层感知器和其他模糊分类方法相媲美。

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