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 …

In this paper, a novel self-adaptive fuzzy learning (SAFL) system is proposed for streaming
data prediction. SAFL self-learns from data streams a predictive model composed of a set of …

This chapter provides an all-round picture of the development and advances in the fields of
evolving fuzzy systems (EFS) and evolving neuro-fuzzy systems (ENFS) which have been …

Zero-sample diagnostic methods have gained recognition in addressing the scarcity of
gearbox fault samples, thereby being regarded as a promising technique to guarantee …

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 …

The antecedent and consequent parts of a first-order evolving intelligent system (EIS)
determine the validity of the learning results and overall system performance. Nonetheless …

This study elaborates on a novel Hidden Markov Model (HMM)-based fuzzy model for time
series prediction. Fuzzy rules (rule-based models) are employed to describe and quantify …

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 …

In this paper, a novel self-learning design of an interval type-2 hierarchical fuzzy system (IT2
HFS) based on rule relevance is proposed. Different from the existing methods, this paper …

Evolving fuzzy systems (EFSs) are a type of adaptive fuzzy rule-based systems which can
self-adapt both their structures and parameters simultaneously. However, the existing EFSs …