Purpose Neuro-fuzzy systems aim to combine the benefits of artificial neural networks and
fuzzy inference systems: a neural network can learn patterns from data and achieves high …

Radial basis function networks (RBFNs) have gained widespread appeal amongst
researchers and have shown good performance in a variety of application domains. They …

Currently, the research in memristor-based associative memory neural networks pays more
attention to classical conditioning and lays less attention to operant conditioning. Moreover …

In this paper, a novel evolving fuzzy-rule-based classifier, termed parsimonious classifier
(pClass), is proposed. pClass can drive its learning engine from scratch with an empty rule …

The idea of meta-cognitive learning has enriched the landscape of evolving systems,
because it emulates three fundamental aspects of human learning: what-to-learn; how-to …

In this paper, we present a sequential projection-based metacognitive learning algorithm in
a radial basis function network (PBL-McRBFN) for classification problems. The algorithm is …

Existing extreme learning algorithm have not taken into account four issues: 1) complexity;
2) uncertainty; 3) concept drift; and 4) high dimensionality. A novel incremental type-2 meta …

This book is motivated by the large gap between the black-box nature of deep learning
architectures and the human interpretability of the knowledge models they encode. It is …

Speech has been one of the major communication medium for years and will continue to do
so until video communication becomes widely available and easily accessible. Although …

Lesions of early cancers often show flat, small, and isochromatic characteristics in medical
endoscopy images, which are difficult to be captured. By analyzing the differences between …