Our site saves small pieces of text information (cookies) on your device in order to deliver better content and for statistical purposes. You can disable the usage of cookies by changing the settings of your browser. By browsing our site without changing the browser settings you grant us permission to store that information on your device.
KURSAD GOKCE1,* , YILMAZ UYAROĞLU2
This paper investigates the stability and tracking performance of discrete-time chaotic systems in the presence of external disturbance and noise. For this purpose, a neural network control scheme is developed on the basis of a novel adaptive learning rate to stabilize the chaotic motion of discrete-time chaotic systems to a fixed point as well as to track the desired reference trajectory. The effectiveness of the proposed method is investigated through simulation studies on 2 dimensional Lozi map and performance comparison has been made with well-known backstepping control strategy..
Adaptive control, Learning systems, Lynapunov, Neural network, Chaos, Nonlinear systems.
KURSAD GOKCE, YILMAZ UYAROĞLU, Adaptive neural network based stabilization and trajectory tracking control of discrete-time chaotic systems, Optoelectronics and Advanced Materials - Rapid Communications, 9, 7-8, July-August 2015, pp.1022-1027 (2015).
Submitted at: April 22, 2015
Accepted at: June 24, 2015
Optoelectronics and Advanced Materials - Rapid Communications
(2022): 0.5 - 2022 Journal Citation Reports (Clarivate Analytics, 2023)
Active accounts were imported to the new platform and assigned a random password by default. In order to access your account you need to access the login page and go through the Lost Password dialog (involves the system sending you an email with instructions) in order to reset your password. This step is necessary as passwords are kept in hashed form and the new platform uses modern hashing algorithms incompatible with the previous ones.