摘要
提出了一种基于粗糙集理论的神经网络,它由传统神经元和粗糙神经元构成。粗糙神经元包含一对传统的神经元,即将数据中的上边界和下边界的作为网络的输入或输出值。当网络的输入和输出不是单值数据而是一个数据集合时,经典的神经网络建立的预测模型的输出就会产生较大的误差,而基于粗糙理论的神经网络则可以很好地解决这个问题,最后对基于粗糙理论的网络进行性能评估。
This paper describes rough neural networks which consists of a combination of rough neurons and conventional neurons. Rough neurons use pairs of upper and lower bounds as values for input and output. In some practical situations, it is preferable to develop prediction models that use ranges as values for input variables. Inability to record precise values of the variables is another situation where ranges of values are associated with a single value of the output variable. The predictions obtained using rough neural networks are significantly better than the conventional neural network model. ;;
出处
《计算机工程》
CAS
CSCD
北大核心
2001年第5期65-67,共3页
Computer Engineering
