【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.更多还原

【Abstract】 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.更多还原