摘要
文中提出一种模糊规则自动生成方法．该方法借助Ｋ－Ｎｅａｒｅｓｔ－Ｎｅｉｇｈｂｏｒ的概念确定控制曲面的关键点，然后根据关键点确定模糊划分，并由此构造模糊神经网络学习模糊规则．神经网络采用ＢＰ算法学习，在学习过程中可根据收敛情况适当增加模糊分区，并重构神经网络继续学习．该方法能生成较精简的规则集。
A method for automatic generation of fuzzy rules is proposed, which finds out the essential points of the control surface by the concept of K Nearest Neighbor, and then uses these points to determine the fuzzy partitions so that it can construct a fuzzy neural network to learn fuzzy rules. The learning algorithm of the neural network is BP algorithm. During the training, the network can increase the number of fuzzy partitions properly due to the condition of the convergence, and then reconstructs itself to learn again. This method can generate a simple and effective rule set, and has a good convergent condition as well as a fast convergent speed.
出处
《计算机研究与发展》
EI
CSCD
北大核心
1999年第2期139-143,共5页
Journal of Computer Research and Development
关键词
模糊规则
自动生成
模糊控制
BP算法
K Nearest Neighbor, essential point, fuzzy partition, fuzzy rules generation, BP algorithm, fuzzy neural network
