Fractional order cell neural network adaptive synchronization control and circuit design method

Abstract

The invention discloses a fractional order cell neural network adaptive synchronization control and circuit design method. A fractional differential algorithm is selected, and the definition of a cell neural network equation is combined, thereby constructing a three-dimensional cell neural network system. The method comprises the steps: designing a drive-response system with a known drive system nonlinear parameter and an unknown response system nonlinear parameter based on the above system, constructing a new adaptive synchronizing controller and a parameter adaptive adjustment rate, and achieving the synchronization of the drive and response systems in numerical simulation; designing the circuit diagrams of the drive and response systems of the fractional order cell neural network, and achieving the circuit simulation of the controller and the adaptive adjustment rate. A simulation result indicates that the circuit simulation and the numerical simulation have the similar synchronous phase diagram, and the method verifies the accuracy of system theoretical analysis and the actual physical realizability. Therefore, the method is practical in the field of engineering.

Description

technical field [0001] The invention belongs to the field of nonlinear chaotic circuit system, and relates to cellular neural network, synchronous control theory and fractional order circuit thought. Background technique [0002] The discovery of chaos is one of the greatest discoveries in physics after the theory of relativity and quantum mechanics in the 20th century. Chaotic motion is a unique motion form of a deterministic nonlinear system, which is expressed as a finite motion from a global perspective. From a local point of view, it is irregular movement. The characteristic of chaos is its extreme sensitivity to disturbances, that is, two chaotic systems starting from slightly different initial conditions will quickly diverge after a certain period of time, and eventually lead to completely different motion trajectories. It is precisely because of the high randomness, unpredictability, and high complexity of chaotic signals, as well as the ease of realization of deter...

AI Technical Summary

Problems solved by technology

Since the fractional order theory was put forward, the fractional order chaotic system has made great progress, such as the complete synchronization and reverse synchronization of the fractional order Chen chaotic system, the research and control of the fractional order Liu chaotic system and its circuit experiment, the fractional order Lorenz chaotic system Chaotic system and its circuit simulation, etc., but there are few studies on the chaotic system of cellular neural network based on fractional order, and there are few reports on the use of fractional order to realize adaptive synchronization control and parameter identification of cellular neural network

Images