Treffer: Extended cascade chaotic systems and estimation parameters with new chaotic grey wolf algorithm.

Regarding to the complex chaotic non-linear behaviour in nature and the impossibility of their accurate modelling with current chaotic models, the development of Cascade Hyper-Chaotic System is necessary. Therefore, in this paper, new cascade hyper chaotic systems are proposed. Cascade hyper-chaotic systems have more parameters than the above-mentioned systems, and in most cases, they show more complex behaviour. On the other hand, due to the complexity of these systems and their high parameters, the identification of this kind of systems is difficult. For this reason, a new Adaptive Chaotic Grey Wolf Optimisation algorithm is presented in this article in order to estimate the parameters of these systems. Numerical simulations have been performed on the above-mentioned systems of Lu-Chen, Chen-Lorenz and Lu-Lorenz cascading. The results show that the systems have more complex behaviour than the seed systems and the proposed grey wolf algorithm is also an effective tool for estimating the parameter of the above-mentioned cascade Hyper-Chaotic System. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Volltext ist im Gastzugang nicht verfügbar. Login für vollen Zugriff.