Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due
to their learning abilities in handling data uncertainties in distributed scenarios. However, it …

Due to the stringent requirements imposed by state-of-the-art technologies, most of modern
spacecrafts are now equipped with very large substructures such as antennas, deployable …

Heterogeneous big data poses many challenges in machine learning. Its enormous scale,
high dimensionality, and inherent uncertainty make almost every aspect of machine learning …

In the supervised learning domain, considering the recent prevalence of algorithms with
high computational cost, the attention is steering towards simpler, lighter, and less …

Uncertainty and distributed nature inherently exist in big data environment. Distributed fuzzy
neural network (D-FNN) that not only employs fuzzy logics to alleviate the uncertainty …

Recently, distributed semisupervised learning (DSSL) algorithms have shown their
effectiveness in leveraging unlabeled samples over interconnected networks, where agents …

The Random-Weight Fuzzy Neural Network is an inference system where the fuzzy rule
parameters of antecedents (ie, membership functions) are randomly generated and the ones …

This paper introduces a sparse learning strategy that is suited for any fuzzy inference model,
in particular to the Adaptive Neuro-Fuzzy Inference System, in order to optimize the …

Nowadays, deep learning is gaining attraction as one of the most successful paradigm for a
plethora of machine learning applications. While its benefits are undoubted, the high …

In this paper, we introduce Random Weights Fuzzy Neural Networks as a suitable tool for
solving prediction problems. The generalization capability of these randomized fuzzy neural …