This paper deal with the dynamics of an autonomous no storage wind-diesel system, comprising a diesel generator and a controlled wind system with a hypo/hyper synchronous cascade. The objective is to maximize the wind ...

In this paper, a possibilistic network implementation for uncertain knowledge modeling of the diagnostic process is proposed as a means to achieve student diagnosis in intelligent tutoring system. This approach is proposed ...

The paper presents an active power filter control method. The reference signal is generated in the stationary reference frame α-β using multiresolution analysis and the controllers used are P-resonant controllers, suitable ...

Generally, control techniques based on multilayer neural networks are used especially in the case of non-linear process and less in case of linear process, because classical control techniques are good enough and relatively ...

In the past years utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in ...

Friction, especially its nonlinear components, may degrade the tracking performance of any servo drive systems. Focused on high precision positioning of DC electrical drive the paper proposes an easy method to compensate ...

The control of shunt active power filters using PWM inverters consists in generating a reference by separating, using different methods, the harmonics to be eliminated. The methods used are time-consuming and need dedicated ...

Proton exchange membrane fuel cell, with low polluting emissions, is a great alternative to replace the traditional electrical power sources for automotive applications or for small stationary consumers. This paper ...

Analog Parallel Architectures like Cellular Neural Networks (CNN’s) have been thoroughly studied not only for their potential in high-speed image processing applications but also for their rich and exciting spatio-temporal ...

Learning a Bayesian network from a numeric set of data is a challenging task because of dual nature of learning process: initial need to learn network structure, and then to find out the distribution probability tables. ...