摘要
本文系统地提出了在模糊评价中构造隶属函数时，应根据各评价因素的特征，选择不同的构造方法，本文采用了统计类比法、待定系数法和多元隶属函数法三种不同方法来构造隶属函数，同时提出了隶属函数修正及其神经网络自学习模型，并将上述思想成功地运用到了具体的评价系统中。
In this paper, the statistic and analogy method, wait coefficient method and multi-element evaluation are used in constructing the subordinate-degree functions. The self-learning medel in neural networks isput forword. The use of the above idea has been proved to be successful.
出处
《中国矿业》
北大核心
1998年第5期68-71,共4页
China Mining Magazine
关键词
隶属函数构造
函数修正
神经网络
模糊评价系统
Construction of subordinate function, Amendment of subordinate function, Self-learning of neural networks
