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 …

Fuzzy rule-based systems (FRBSs) is a rule-based system which uses linguistic fuzzy
variables as antecedents and consequent to represent human-understandable knowledge …

High-dimensional data classification is widely considered as a challenging task in machine
learning due to the so-called “curse of dimensionality.” In this article, a novel multilayer …

Ensemble learning is a widely used methodology to build powerful predictors from multiple
individual weaker ones. However, the vast majority of ensemble learning models are …

In order to process the high-dimensionally complicated problems, the intelligence systems
need to go deeper to learn high-level data representation. In this article, based on the …

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data
prediction. In this article, a novel zero-order EFS with a unique belief structure is proposed …

In this paper, an interval type-2 evolving fuzzy Kalman filter (IT2EFKF) is designed for
processing unobservable spectral components of uncertain experimental data. The primary …

An important constraint of Fuzzy Inference Systems (FIS) is their structured rules. Indeed,
each rule should evaluate all input variables in its antecedent part. Thus, the length of all …

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated
strong performance on many real-world problems concerning data stream classification …

Many industrial processes are full of nonlinearity and noise. Fuzzy modeling often leads to
issues with dimension disaster and poor robustness for such processes. To address these …