  Prior work introduced a gradient descent trained expert system that
conceptually combines the learning capabilities of neural networks with the
understandability and defensible logic of an expert system. This system was
shown to be able to learn patterns from data and to perform decision-making at
levels rivaling those reported by neural network systems. The principal
limitation of the approach, though, was the necessity for the manual
development of a rule-fact network (which is then trained using
backpropagation). This paper proposes a technique for overcoming this
significant limitation, as compared to neural networks. Specifically, this
paper proposes the use of larger and denser-than-application need rule-fact
networks which are trained, pruned, manually reviewed and then re-trained for
use. Multiple types of networks are evaluated under multiple operating
conditions and these results are presented and assessed. Based on these
individual experimental condition assessments, the proposed technique is
evaluated. The data presented shows that error rates as low as 3.9% (mean, 1.2%
median) can be obtained, demonstrating the efficacy of this technique for many
applications.
