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Title: Rule generation from neural networks

The neural network approach has proven useful for the development of artificial intelligence systems. However, a disadvantage with this approach is that the knowledge embedded in the neural network is opaque. In this paper, we show how to interpret neural network knowledge in symbolic form. We lay down required definitions for this treatment, formulate the interpretation algorithm, and formally verify its soundness. The main result is a formalized relationship between a neural network and a rule-based system. In addition, it has been demonstrated that the neural network generates rules of better performance than the decision tree approach in noisy conditions. 7 refs.
Authors:
 [1]
  1. Univ. of Florida, Gainesville, FL (United States)
Publication Date:
OSTI Identifier:
135309
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Systems, Man, and Cybernetics; Journal Volume: 24; Journal Issue: 8; Other Information: PBD: Aug 1994
Country of Publication:
United States
Language:
English
Subject:
99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; ARTIFICIAL INTELLIGENCE; ALGORITHMS; NEURAL NETWORKS; DESIGN; KNOWLEDGE BASE