The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We have previously showed that it is possible to achieve parameter identification of discrete-time structured uncertainties without requiring persistency of excitation when using Concurrent Learning. Instead, granted a less restrictive condition compared to that of persistency of excitation is verified, exponential convergence of parameter estimates to their true values ensues. The present study applies...
Anti-Malware industry faces the challenge of evaluating huge amount of data for potential malicious contents. This is due to the fact that hackers introduce polymorphism to the existing malicious groups/classes. Effective feature extraction and classification of malware data is necessary to tackle such issues. In this paper, we visualize viruses in an image as they capture minor changes while retaining...
In this paper, we revisit the Proportional-Integral-Derivative (PID) controller design for torque control of robotic manipulators, for which, appropriate tuning of the said controller could prove very burdensome, especially with increasing degrees-of-freedom (DOF) and/or when designing a Multi-Input Multi-Output (MIMO) PID controller. That is, when generating and tuning matrix P-I-D gains as opposed...
Concurrent Learning has been previously used in continuous-time uncertainty estimation problems and adaptive control to solve the parameter identification problem without requiring persistently exciting inputs. Specifically selected past data are jointly combined with current data for adaptation. Here, we extend the parameter identification problem results of Concurrent Learning for structured uncertainties...
Using discrete-time adaptive control tools found in literature, we develop an adaptive particle swarm optimizer. We show, using Lyapunov's direct method, that our devised error system is ultimately uniformly bounded, ultimately goes to zero by Lasalle-Yoshizawa theorem, and our optimizer is stable.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.