of dust ion acoustic shock waves in dusty plasma using Cellular Neural Network

Abstract
The Cellular Neural Network (CNN) is implemented to investigate the features of dust ion acoustic
shock waves in a two-fluid model of magnetized dusty plasma. The electrons in this model obey the
hybrid Cairns-Tsallis distribution. The reductive perturbation method is used to derive the
corresponding Zakharov Kuznetsov Burger (ZKB) equation. Then, the CNNalgorithm is integrated
with the Finite Difference Method to simulate the ZKB equation with a high accuracy. The obtained
solution is approximately identical to the analytical solution which is obtained from the Tanh method.
An algorithm to solve ZKB equation using the Finite Difference Method is employed to asses the
accuracy of the CNNmethod. Moreover, it is found that the plasma parameters (viscosity coefficients,
cyclotron frequency, nonextensive parameter,Ketc.) have significant effects on the shock wave
characteristics.