A new methodology of extraction, optimization, and application of sets of logical rules is
described. Neural networks are used for initial rule extraction, local or global minimization …

In many applications, black-box prediction is not satisfactory, and understanding the data is
of critical importance. Typically, approaches useful for understanding of data involve logical …

The advent of artificial neural networks has contributed significantly to the field of knowledge
engineering. Neural networks belong to a family of models that are based on a learning-by …

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 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 …

Hybrid intelligent systems that combine knowledge-based and artificial neural network
systems typically have four phases, involving domain knowledge representation, mapping of …

2. Some Background Information 49 3. Definitions and Terminology 52 4. The One Clause at
a Time (OCAT) Approach 54 4. 1 Data Binarization 54 4. 2 The One Clause at a Time …

Rule extraction from neural networks is the task for obtaining comprehensible descriptions
that approximate the predictive behavior of neural networks. Rule-extraction algorithms are …

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 …

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 …