Rule generation from neural networks

1994/01/01 by LiMin Fu · 9 citations
Computer Science · Engineering · #Neural Networks and Applications #Fuzzy Logic and Control Systems #Fault Detection and Control Systems

paper · doi:10.1109/21.299696

Abstract

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 dawn 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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by

• A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
• Neural Decision Trees
• Learning Causal State Representations of Partially Observable Environments
• On Interpretability of Artificial Neural Networks: A Survey
• Generalized Constraints as A New Mathematical Problem in Artificial Intelligence: A Review and Perspective
• Principles and Practice of Explainable Machine Learning. [europepmc]
• Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation. [europepmc]
• Cleavage Site Analysis Using Rule Extraction from Neural Networks [europepmc]
• Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction [europepmc]

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