A single dendritic neuron model (DNM) that owns the nonlinear information processing
ability of dendrites has been widely used for classification and prediction. Complex-valued …

Abstract The Random Vector Functional Link Neural Network (RVFLNN) enables fast
learning through a random selection of input weights while learning procedure determines …

In this article, a brain-inspired winner-take-all emotional neural network (WTAENN)
architecture is proposed and then the universal approximation property for this kind of …

In this paper, we present a complex-valued neuro-fuzzy inference system (CNFIS) and
develop its meta-cognitive learning algorithm. CNFIS has four layers–an input layer with m …

In this article, a competitive functional link artificial neural network (C-FLANN) is proposed
for function approximation and classification problems. In contrast to the traditional functional …

Many existing results on fault-tolerant algorithms focus on the single fault source situation,
where a trained network is affected by one kind of weight failure. In fact, a trained network …

Brain emotional learning (BEL) methods are a recently developed class of emotional brain-
inspired algorithms, that enjoy feed-forward computational complexity on the order of O (n) …

The functional link adaptive filter (FLAF) represents an effective solution for online nonlinear
modeling problems. In this paper, we take into account a FLAF-based architecture, which …

Random vector functional link neural network (RVFL) is an important noniterative neural
model due to its fast learning speed, universal approximation capability, and excellent …

There are three important concepts related to learning processes in neural networks:
reducibility, nonminimality, and singularity. Although the definitions of these three concepts …