摘要
本文提供了一种比模糊推理更为自然的方式使用人们的经验知识,通过一组神经元不同程度的兴奋表达一个抽象的概念值,由此将抽象的经验规则转化成多层神经网络的输入-输出样本.通过Back-Propagation学习算法使得网络记忆这些样本。控制器以“联想记忆”方式使用这些经验.本文介绍了控制器的构造方法,给出了控制仿真结果,并讨论了这种控制器的特点和发展前途.
A more natural way of using the human experiences than the fuzzy reasoning is provided in this paper. An abstract concept is expressed by a set of neurons with different exciting degrees. So, the abstract experience rules are transformed to the input-output samples of multi layer neural network, and these samples are recorded in the network by Back-Propagation algorithm. The controller utilizes these experiences according to associative memory. The design, simulation result, feature and further development of this controller are also discussed.
出处
《自动化学报》
EI
CSCD
北大核心
1991年第1期63-67,共5页
Acta Automatica Sinica
基金
国家自然科学基金 No.68974016
关键词
神经网络
模糊规则
记忆
智能控制
Neural network
intelligent control
back-propagation
fuzzy control
