The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule
generation algorithms. Rule generation from artificial neural networks is gaining in …

This chapter introduces the basics of neural-symbolic systems used thoughout the book. A
brief bibliographical review is also presented. Neural-symbolic systems have become a very …

This paper presents an extension of the method presented by Benitez et al (1997) for
extracting fuzzy rules from an artificial neural network (ANN) that express exactly its …

In the research of rule extraction from neural networks, fidelity describes how well the rules
mimic the behavior of a neural network while accuracy describes how well the rules can be …

In the past, neural networks have been viewed as classification and regression systems
whose internal representations were extremely difficult to interpret. It is now becoming …

Active research into processes and techniques for extracting the knowledge embedded
within trained artificial neural networks has continued unabated for almost ten years. Given …

A method to extract a fuzzy rule based system from a trained artificial neural network for
classification is presented. The fuzzy system obtained is equivalent to the corresponding …

A constraint satisfaction neural network (CSNN) approach is proposed for breast cancer
diagnosis using mammographic and patient history findings. Initially, the diagnostic decision …

We study the problem of computing the preimage of a set under a neural network with
piecewise-affine activation functions. We recall an old result that the preimage of a …

Despite the fact that artificial neural networks (ANNs) are universal function approximators,
their black box nature (that is, their lack of direct interpretability or expressive power) limits …