Chaos synchronization using adaptive quantum neural networks and its application in secure communication and cryptography

Adaptive control for memristive system via compensatory controller and Chebyshev neural network

In this paper, based on linear matrix inequality technique, a simple controller and a compensatory controller are designed. It can track arbitrary fixed points and any periodic orbits. In addition, a synchronization control method via Chebyshev neural network with external disturbances is proposed. An adaptive controller is given. The Chebyshev neural network is used to approximate the uncertain nonlinear function and the adaptive law is used to adjust the corresponding parameters in the system. Taking a 4D memristive chaotic system as examples, the results are in consistent with the simulations. From a framework and control theoretical point of view, the proposed synchronization approach via compensation controller and Chebyshev neural network is firstly presented. From an application point of view, the proposed scheme can simplify the complexity of controller design. It is promising in many applications for mem-systems as secure communications and neural networks.

Hybrid parameters for fluid identification using an enhanced quantum neural network in a tight reservoir

This paper proposes a fluid classifier for a tight reservoir using a quantum neural network (QNN). It is difficult to identify the fluid in tight reservoirs, and the manual interpretation of logging data, which is an important means to identify the fluid properties, has the disadvantages of a low recognition rate and non-intelligence, and an intelligent algorithm can better identify the fluid. For tight reservoirs, the logging response characteristics of different fluid properties and the sensitivity and relevance of well log parameter and rock physics parameters to fluid identification are analyzed, and different sets of input parameters for fluid identification are constructed. On the basis of quantum neural networks, a new method for combining sample quantum state descriptions, sensitivity analysis of input parameters, and wavelet activation functions for optimization is proposed. The results of identifying the dry layer, gas layer, and gas-water co-layer in the tight reservoir in the Sichuan Basin of China show that different input parameters and activation functions affect recognition performance. The proposed quantum neural network based on hybrid parameters and a wavelet activation function has higher fluid identification accuracy than the original quantum neural network model, indicating that this method is effective and warrants promotion and application.

Projective Synchronization of Delayed Uncertain Coupled Memristive Neural Networks and Their Application

In this article, the authors analyzed the nonlinear effects of projective synchronization between coupled memristive neural networks (MNNs) and their applications. Since the complete signal transmission is difficult under parameter mismatch and different projective factors, the delays, which are time-varying, and uncertainties have been taken to realize the projective synchronization of MNNs with multi-links under the nonlinear control method. Through the extended comparison principle and a new approach to dealing with the mismatched parameters, sufficient criteria have been determined under different types of projective factors and the framework of the Lyapunov-Krasovskii functional (LKF) for projective convergence of the coupled MNNs. Instead of the classical treatment for secure communication, the concept of error of synchronization between the drive and response systems has been applied to solve the signal encryption/decryption problem. Finally, the simulations in numerical form have been demonstrated graphically to confirm the adaptability of the theoretical results.

H∞ master-slave synchronization for delayed impulsive implicit hybrid neural networks based on memory-state feedback control

This paper investigates the H∞ master-slave synchronization problem for delayed impulsive implicit hybrid neural networks based on memory-state feedback control. By developing a more holistic stochastic impulse-time-dependent Lyapunov-Krasovskii functional and dealing with the nonlinear neuron activation function, the stochastic admissibility and prescribed H∞ performance index for the synchronization error closed-loop system are achieved. In addition, the desired mode-dependent memory-state feedback synchronization controller is acquired in the form of linear matrix inequalities. The free-weighting matrix technique is adopted to remove the inherent limitation of time-varying delay derivative for the implicit delayed systems, and the derivative of time-varying delay is relaxed enough to be greater than 1. The simulation of genetic regulatory network in bio-economic system is given to verify validity of the derived results.

Synchronization of Hyperchaotic Systems Based on Intermittent Control and its Application in Secure Communication

The synchronization of master-slave hyperchaotic systems is investigated by intermittent control and proved by the Lyapunov stable theory. Meanwhile, a new secure communication scheme is designed for the continuous and digital information. The encrypted information is transmitted to receiver through the intermittent controller, which reduces the disturbance to the synchronization of master-slave systems and improves the security and reliability of secure communication. Before transmitting to the receiver, the continuous signal is firstly modulated and masked by the chaotic signals. Furthermore, an encryption algorithm for the digital information of color image is proposed by the pseudo-random sequences of Chen hyperchaotic system, and then the encrypted image is modulated and masked by the variables of the master system. The original image can be decrypted successfully at the receiving end after the slave system is synchronized with the master system. Finally, the feasibility and effectiveness of this scheme are verified by simulation experiments. In addition, the security analysis of the image encryption algorithm is also discussed, such as key sensitivity, correlation coefficient, NIST test, and return map.

Complex Modified Projective Difference Function Synchronization of Coupled Complex Chaotic Systems for Secure Communication in WSNs

Complex-variable chaotic systems (CVCSs) have numerous advantages over real-variable chaotic systems in chaos communication due to their increased unpredictability, confidentiality, and the ease of implementation. Synchronization between the master and slave systems in CVCSs is key to achieving encryption and decryption. However, existing synchronization schemes for CVCSs require the amplitude of the chaotic signal to be much larger than that of the plaintext. Moreover, traditional chaotic masking of complete synchronization (CS) requires uniformity between the transmitter and receiver ends. Therefore, we propose a complex modified projective difference function synchronization (CMPDFS) of CVCSs to address these issues, where the modified projective matrix helps address the issues with the amplitude. The receiver end is reconstructed without uniformity of the transmitter. We design the CMPDFS controller and propose a new secure communication scheme for wireless sensor networks (WSNs). The basic principle is fundamentally different from traditional chaotic masking. Simulation results and security analysis demonstrate that the CMPDFS communication scheme has a large key space, high sensitivity to encryption keys, high security, and an acceptable encryption speed. Hence, the proposed scheme can improve the security of WSNs. Moreover, it also can be applied to similar communication systems.