This paper presents a review of the central theories involved in hybrid models based on
fuzzy systems and artificial neural networks, mainly focused on supervised methods for …

Neuro-fuzzy systems have attracted the growing interest of researchers in various scientific
and engineering areas due to its effective learning and reasoning capabilities. The neuro …

Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …

Behavioral economics tells us that emotions can profoundly affect individual behavior and
decision-making. Does this also apply to societies at large, ie can societies experience …

In this paper, we apply sentiment analysis and machine learning principles to find the
correlation between” public sentiment” and” market sentiment”. We use twitter data to predict …

In today's industrial systems, economic markets, life and health-care sciences fuzzy systems
play an important role in many application scenarios such as system identification, fault …

Most of the dynamics in real-world systems are compiled by shifts and drifts, which are
uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …

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 …

This paper focuses on the study of synchronization problem for TS fuzzy memristive neural
networks with time delay. First, a delay-independent nonlinear fuzzy control is designed …