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.
Computers in biology and medicine 24(6), pp.419-429