Rule extraction (RE) from recurrent neural networks (RNNs) refers to finding models of the
underlying RNN, typically in the form of finite state machines, that mimic the network to a …

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 …

The rule extraction capability of neural networks is an issue of interest to many researchers.
Even though neural networks offer high accuracy in classification and prediction, there are …

To date, the preponderance of techniques for eliciting the knowledge embedded in trained
artificial neural networks (ANN's) has focused primarily on extracting rule-based …

Recurrent neural networks that are trained to behave like deterministic finite-state automata
(DFAs) can show deteriorating performance when tested on long strings. This deteriorating …

This paper examines the inductive inference of a complex grammar with neural networks
and specifically, the task considered is that of training a network to classify natural language …

Rule‐based and case‐based reasoning are two popular approaches used in intelligent
systems. Rules usually represent general knowledge, whereas cases encompass …

Learning long-term temporal dependencies with recurrent neural networks can be a difficult
problem. It has recently been shown that a class of recurrent neural networks called NARX …

Abstract Recurrent Neural Networks (RNNs) have achieved tremendous success in
processing sequential data, yet understanding and analyzing their behaviours remains a …

There has been an increased interest in combining fuzzy systems with neural networks
because fuzzy neural systems merge the advantages of both paradigms. On the one hand …