Non-Gaussian randomness widely exists in complex dynamical systems, in which the
traditional mean-variance index cannot fully reflect the systematic characteristics. To …

Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …

Stochastic configuration network (SCN), as a novel incremental generation model with
supervisory mechanism, has an excellent superiority in solving large-scale data regression …

Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

As a class of randomized learner model, stochastic configuration networks (SCNs) have
been successfully applied in a few data analytics tasks. Given the industrial big data …

Incremental random weight networks (IRWNs) face the issues of weak generalization and
complicated network structure. There is an important reason: the learning parameters of …

Stochastic configuration networks (SCNs) have been widely used as predictive models to
model complex nonlinear systems due to their advantages in terms of easy-to-implement …

This article proposes a dynamic evolving fuzzy system (DEFS) for streaming data prediction.
DEFS utilizes the enhanced data potential and prediction errors of individual local models …

Evolving systems unfolds from the interaction and cooperation between systems with
adaptive structures, and recursive methods of machine learning. They construct models and …

Accurate reference crop evapotranspiration (ET 0) estimation is essential for agricultural
water management, crop productivity, and irrigation systems. As the standard ET 0 …