Memristor-based Chaotic System Design And Its Application

With the rapid development of information society,intelligent information processing,security information transmission,integrated information systems and other requirements become the focus of the current scientific and technological development.Nano-sized memristor with non-linear nonvolatile and other characteristics,the emergence of memristor brings new solutions for intelligent information processing.The memristor is introduced into the design of the non-linear circuit,memristive chaotic system has a richer nonlinear characteristic,the generated chaotic signal has a better pseudo-random performance,and the power consumption and volume are more dominant than the traditional chaotic system.It has a wide application prospect in the fields of image encryption,spread spectrum communication and secure communication.In this dissertation,the mathematical and circuit models of the memristor are studied,and their basic electrical and nonlinear properties are analyzed.Combining the memristor with chaotic system,the memristor-based chaotic system and its corresponding circuit are designed.Subsequently,the memristor is used in artificial neural network and the memristor cellular neural network is established.This dissertation mainly includes the following four parts:(1)The mathematical model of memristor is studied,and the corresponding PSPICE circuit model is established.The physical mechanism is verified by numerical simulation and circuit simulation,and the electrical characteristics are verified.(2)A memristive chaotic system is introduced to generate a kind of heart-shaped attractors.Different from the previous memristive chaotic system,the proposed chaotic system not only utilizes the nonlinear characteristic of the memristor,but also utilizes its adjustable characteristic.When the polarity of the memristor is changed,a mirror image of the chaotic attractors will appear in the system.And the system can be switched between chaotic states,periodic states and stable states as the memristor parameters are adjusted,so the system can be applied to both the practice on generating chaos signal and suppressing chaotic interference.Some basic properties such as Poincaré-map,Lyapunov index and bifurcation diagram are presented,either analytically or numerically.In addition,an analog implementation based on PSPICE simulation is also designed.(3)A new memristor-based time-delayed chaotic system is proposed.The corresponding parameter region of the stable equilibrium point of the system is discussed in detail.Then,the affection of parameter variation on the dynamic behavior of the system is discussed,and a series of phase diagrams with different time-delayed parameter and system parameter is described by numerical simulation.Moreover,the proposed time-delayed system is used to generate pseudo random sequences,and the experimental results show that the proposed system has good self-correlation,cross-correlation,and the significant approximate entropy.The proposed new time-delayed chaotic system has complex dynamic behavior and good randomness,which can meet the needs of the application of spread spectrum communication,image encryption and many other fields.(4)A new memristor-based cellular neural network is proposed.We have improved the traditional memristive bridge circuit,besides the advantages of the traditional synaptic bridge circuit,the improved memristive bridge circuit has a more simplified circuit and simplified weight change conditions.At the same time,we use PSPICE circuit simulation to achieve the operation of synaptic weight.In addition,we apply the memristor-based cellular neural network to image denoising and edge extraction.Experimental results show that the memristor-based cellular neural network has a good effect in the application of image processing.The proposed memristor-based cellular neural network can reduce the size of circuit and improve the speed of operation,and the circuit structure is more compact and universal,which helps to promote the hardware implementation of artificial neural network.