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 …

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 …

An approach to the Colored Petri Nets (CPN)-based control is proposed in this paper. CPN
are used for modeling the dynamics of both the controller and the controlled process in the …

Abstract Relying on the Robust Evolving Cloud-based Control (RECCo) protocol, a
decentralized evolving fuzzy control scheme is presented in this paper for a strongly …

The fuzzy logic controller (FLC) makes it possible to control a system using IF-THEN rules
through human intellect. It tackles parameter uncertainty using imprecise reasoning. The …

In this paper, an approach to the identification of hybrid systems is discussed. It is based on
the incremental fuzzy C-regression clustering. Based on the distance between the current …

In this article, a novel self-evolving data cloud-based proportional integral derivative
(PID)(SEDCPID) like controller is proposed for uncertain nonlinear systems. The proposed …

The feedback linearization is a powerfull nonlinear method based on the principle of
canceling the nonlinearities of the system model. However, if the model differs from the real …

This paper presents an adaptive nonparametric evolving fuzzy controller for uncertain
nonlinear systems with dead zone. The unknown nonlinear-ities caused by the dead zone …

This paper introduces a method for identifying hybrid systems using fuzzy C-regression
clustering. A distinctive aspect of this approach is the use of hyperplanes as cluster …