Robust synchronisation of uncertain chaotic neural networks with time-varying delay via stochastic sampled-data controller

Author(s):  
Li Yue ◽  
Zhao Yixin ◽  
Huang Wei
Author(s):  
Abdujelil Abdurahman ◽  
Malika Sader ◽  
Haijun Jiang

AbstractCompared to other types of synchronization such as complete synchronization and lag synchronization, there is a unique advantage in projective synchronization since it can greatly improve the security of communication. In this paper, the projective synchronization problem of a class of chaotic neural networks with time-varying delay is investigated via designing a novel adaptive controller. Some simple and useful criteria are derived by employing Lyapunov functional method and Lagrange mean value theorem. Finally, an example and its numerical simulations are given to demonstrate the effectiveness of the proposed control schemes. It is worth to mention that the designed controller in this paper dos not require any knowledge about the activation functions, which can be seen the main novelty of the paper.


2020 ◽  
Vol 24 (19) ◽  
pp. 14743-14755
Author(s):  
Zifang Qu ◽  
Zhengdi Zhang ◽  
Zhenbin Du ◽  
Tiebiao Zhao

2013 ◽  
Vol 380-384 ◽  
pp. 2623-2628
Author(s):  
Xin Guo Zou

Recently, a large number of chaotic cryptosystems have been proposed, many of them fundamentally flawed by lack of robustness and security. In this paper, we point out the weakness of a very recent block cipher algorithm which is based on the chaotic Hopfield neural networks with time varying delay and give the improved scheme of it. We provide the chosen plaintext attack to recover the permuted plaintext string, and point out that the encryption speed is not fast enough. It is shown that the dependence on the key, but not on the plaintext, to generate binary sequences of iterating the chaotic map mechanism facilitates leakage of the information and vulnerable been attacked. Based on such a fact, we give the improved scheme to obtain higher security and faster speed.


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