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 …

Time series prediction with neural networks has been the focus of much research in the past
few decades. Given the recent deep learning revolution, there has been much attention in …

The COVID-19 pandemic continues to have major impact to health and medical
infrastructure, economy, and agriculture. Prominent computational and mathematical models …

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 …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …

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 …

Acquire the tools for understanding new architectures and algorithms of dynamical recurrent
networks (DRNs) from this valuable field guide, which documents recent forays into artificial …

A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed. The
RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic …

The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks.
Training algorithms for Recurrent Neural Networks, based on the error gradient, are very …

The theory of finite automata on finite stings, infinite strings, and trees has had a dis
tinguished history. First, automata were introduced to represent idealized switching circuits …