Most of the dynamics in real-world systems are compiled by shifts and drifts, which are
uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in …

Fuzzy neural networks are connectionist systems that facilitate learning from data, reasoning
over fuzzy rules, rule insertion, rule extraction, and rule adaptation. The concept of a …

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

In this paper, a novel fuzzy neural network termed as dynamic parsimonious fuzzy neural
network (DPFNN) is proposed. DPFNN is a four layers network, which features coalescence …

In this paper, a novel learning algorithm for dynamic fuzzy neural networks based on
extended radial basis function neural networks, which are functionally equivalent to Takagi …

In this article, a new self-organizing fuzzy neural network (FNN) model is presented which is
able to simultaneously and accurately learn and reproduce different sequences. Multiple …

One of the key differences between the learning mechanism of humans and Artificial Neural
Networks (ANNs) is the ability of humans to learn one task at a time. ANNs, on the other …

As one of the three pillars in computational intelligence, fuzzy systems are a powerful
mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy …

There are two main approaches to design a neural fuzzy system; namely, through expert
knowledge, and through numerical data. While the computational structure of a system is …

Broad Learning System (BLS) that aims to offer an alternative way of learning in deep
structure is proposed in this paper. Deep structure and learning suffer from a time …