Abstract
Attempts have been made to incorporate symbolic knowledge into neural networks, resulting in a class of networks known as knowledge-based neural networks. These constructions inherit the merits of both the knowledge-based and neural network approaches to the modeling of human intelligence, and yet the hybrid holds out a major promise of being more successful than its parents. The paper introduces three kinds of knowledge-based neural network. These are based on production rules, decision trees, and semantic constraints, respectively.
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