The authors integrate two well-known systems, the Rssler and Lorentz systems,to introduce a new chaotic system, called the Lorentz-Rssler system. Then, taking into account the effect of environmental noise, the au...The authors integrate two well-known systems, the Rssler and Lorentz systems,to introduce a new chaotic system, called the Lorentz-Rssler system. Then, taking into account the effect of environmental noise, the authors incorporate white noise in both Rssler and Lorentz systems to have a corresponding stochastic system. By deriving the uniform a priori estimates for an approximate system and then taking them to the limit,the authors prove the global existence, uniqueness and the pathwise property of solutions to the Lorentz-Rssler system. Moreover, the authors carried out a number of numerical experiments, and the numerical results demonstrate their theoretic analysis and show some new qualitative properties of solutions which reveal that the Lorentz-Ro¨ssler system could be used to design more complex and more secure nonlinear hop-frequence time series.展开更多
基金supported by the National Natural Science Foundation of China(Nos.11101044,11371065)the Beijing Center for Mathematics and Information Interdisciplinary Sciences
文摘The authors integrate two well-known systems, the Rssler and Lorentz systems,to introduce a new chaotic system, called the Lorentz-Rssler system. Then, taking into account the effect of environmental noise, the authors incorporate white noise in both Rssler and Lorentz systems to have a corresponding stochastic system. By deriving the uniform a priori estimates for an approximate system and then taking them to the limit,the authors prove the global existence, uniqueness and the pathwise property of solutions to the Lorentz-Rssler system. Moreover, the authors carried out a number of numerical experiments, and the numerical results demonstrate their theoretic analysis and show some new qualitative properties of solutions which reveal that the Lorentz-Ro¨ssler system could be used to design more complex and more secure nonlinear hop-frequence time series.
