Su M C
Department of Electrical Engineering, Tamkang University, Taiwan, R.O.C.
Comput Biol Med. 1994 Nov;24(6):419-29. doi: 10.1016/0010-4825(94)90040-x.
A major bottleneck in building expert systems is the process of acquiring the required knowledge in the form of production rules. A novel class of neural networks is proposed to articulate the knowledge it learned from a set of examples. It provides an appealing solution to the problem of knowledge acquisition. After training, the knowledge embedded in the numerical weights of trained neural networks can be easily extracted and represented in the form of production rules. The approach is demonstrated by an example of a hypothesis regarding the pathophysiology of diabetes.
构建专家系统的一个主要瓶颈是获取以产生式规则形式存在的所需知识的过程。提出了一类新颖的神经网络来阐明它从一组示例中学到的知识。它为知识获取问题提供了一个有吸引力的解决方案。训练后，嵌入在经过训练的神经网络数值权重中的知识可以很容易地提取出来，并以产生式规则的形式表示。通过一个关于糖尿病病理生理学假设的例子对该方法进行了演示。
