Network Structuring and Training Using Rule-based Knowledge

We demonstrate in this paper how certain forms of rule-based knowledge can be used to prestructure a neural network of normalized basisfunctions and give a probabilistic interpretation of the network architecture. We describe several ways to assure that rule-based knowledge is preserved during training and present a method for complexity reduction that tries to minimize the number ofrules and the number of conjuncts. After training the refined rules are extracted and analyzed.