The Research Of Chaos Control And Synchronization

Chaos is a random phenomenon in deterministic nonlinear dynamic systems. Because of the break through of the concept, chaos is the foreland of today's science. The model of cellular neural network (CNN) is also a research hotspot in recent years. But little work about connecting the chaotic systems and CNN has been done especially the research about how to realize the tracking control of CNN. This thesis focuses on chaos control and chaotic synchronization, tries to connect related content of chaos theory with the model of cellular neural network, realizes the communication security between two cellular neural networks and confirms the validity of a novel algorithm which is applied to the synchronization research in cellular neural networks. All of these are rarely involved in former articles. In this paper, some particular works in following aspects have been done:(1) A novel tracking control method is proposed for the CNN model and the unified chaotic system. This method enables the signal of the model approach to any desired smooth orbit at an exponent rate. Based on this, the synchronization of such CNN model with itself and with another system is analyzed. Computer simulations prove the effectiveness of the proposed method.(2) The chaotic phenomenon in neural networks is described. According to modern control theory, the synchronization of a type of cellular neural network (CNN) is achieved, which is applied to communication security. Additionally by using a novel algorithm, the synchronization trial of some chaotic nodes is successful. Each of these nodes is a cellular neural network.(3) Synchronization research on the circuit model of Chua system has been discussed. Two common methods in chaotic control—linear feedback control and adaptive feedback control have been compared.