As a paradigm of data processing, granular computation concerns processing complex data
entities called granules, which arise from data abstraction and derivation of knowledge from …

In this article, we investigate the issue of the accelerated adaptive fuzzy optimal control of
three coupled fractional-order chaotic electromechanical transducers. A small network …

Big Data advancements motivate researchers to develop and improve intelligent models to
deal efficiently and effectively with data. In this scenario, time series forecasting obtains even …

Recognizing the weakness of the existing evolving fuzzy systems (EFSs) where the
selection and determination of thresholds for the structure and parameter learning are …

The algorithms utilized for the big data learning must satisfy three conditions to improve the
performance in the processing of big quantity of data:(1) they need to be compact,(2) they …

This paper proposes an online learning algorithm for data streams, namely self-evolving
fuzzy system (SEFS). Unlike the fixed control parameters commonly used in evolving fuzzy …

Currently, the increase in devices capable of continuously collecting data on non-stationary
and dynamic variables affects predictive models, particularly if they are not equipped with …

Equity asset volatility modeling and forecasting provide key information for risk
management, portfolio construction, financial decision making, and derivative pricing …

This paper proposes a new interval type-2 fuzzy logic system (IT2 FLS) for financial
investment with time-varying parameters adaptive to real-time data streams by using an on …

In this paper, the problem of learning in big data is considered. To solve this problem, a new
algorithm is proposed as the combination of two important evolving and stable intelligent …