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

2016 ◽  
Author(s):  
Li Yue ◽  
Zhao Yixin ◽  
Huang Wei
Keyword(s):  
Neural Networks ◽  
Time Varying ◽  
Sampled Data ◽  
Chaotic Neural Networks ◽  
Time Varying Delay ◽  
Data Controller ◽  
Varying Delay

Improved Results on Adaptive Control Approach for Projective Synchronization of Neural Networks with Time-Varying Delay

2019 ◽  
Vol 20 (6) ◽  
pp. 623-631 ◽  
Author(s):  
Abdujelil Abdurahman ◽  
Malika Sader ◽  
Haijun Jiang
Keyword(s):  
Neural Networks ◽  
Functional Method ◽  
Mean Value ◽  
Projective Synchronization ◽  
Mean Value Theorem ◽  
Time Varying ◽  
Chaotic Neural Networks ◽  
Control Approach ◽  
Time Varying Delay ◽  
Varying Delay

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.

Fuzzy optimal control for nonlinear systems with time-varying delay via sampled-data controller

2020 ◽  
Vol 24 (19) ◽  
pp. 14743-14755
Author(s):  
Zifang Qu ◽  
Zhengdi Zhang ◽  
Zhenbin Du ◽  
Tiebiao Zhao
Keyword(s):  
Optimal Control ◽  
Nonlinear Systems ◽  
Time Varying ◽  
Sampled Data ◽  
Time Varying Delay ◽  
Data Controller ◽  
Varying Delay

An Improved Block Cryptosystem Based on Iterating Delayed Chaotic Neural Networks

2013 ◽  
Vol 380-384 ◽  
pp. 2623-2628
Author(s):  
Xin Guo Zou
Keyword(s):  
Neural Networks ◽  
Block Cipher ◽  
Chaotic Map ◽  
Binary Sequences ◽  
Time Varying ◽  
Chosen Plaintext Attack ◽  
Chaotic Neural Networks ◽  
Time Varying Delay ◽  
Delayed Chaotic Neural Networks ◽  
Varying Delay

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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