Finite-time Synchronization Of Two Kinds Of Neural Networks With Time Delays

In this paper,we establish some new sufficient conditions of the finite-time synchronization for a class of drive-response BAM neural network with time delays and a class of drive-response complex-valued neural network with time delays.Without using some finite-time stability theorems,linear matrix inequality techniques which are often used to investigate the finite-time synchronization for neural networks in existing articles,we use new methods: integrating factor method and integral inequality techniques.Our methods and results are novel,which are the supplement and innovation to the existing neural network finite-time synchronization results and methods.This paper is divided into four chapters,the main contents are as follows:In chapter 1,we introduce the research progress and significance of the synchronization of BAM neural network with time delays and complex-valued neural network with time delays,and point out the main content and innovation of this paper.In chapter 2,we discuss the finite-time synchronization problem for a class of drive-response BAM neural network with time delays.Firstly,the definition and model of finite-time synchronization of time-delay drive-response BAM neural network and related theoretical knowledge are proposed.Second,we prove that the limit of the error system is zero by designing the appropriate controllers,constructing two V function associated with the error and using the method of integral transformation and integral inequality.So the sufficient conditions for the finite-time synchronization of driveresponse BAM neural network with time delays are obtained.In chapter 3,we study the finite-time synchronization problem for a class of driveresponse complex-valued neural networks with time delays.First of all,the complexvalued states,parameters and activation functions in the system equations are separated into the real and imaginary parts,so the system equation is transformed into real value differential equation system.Secondly,we use the integral inequality techniques to find the relationship between the two V functions,and show the limit of the error system in finite time is zero,thus we establish some new sufficient conditions of finite-time synchronization for the complex-valued neural networks with time delays.Our results are the generalization of the finite-synchronization conclusion of the drive-response system in real domain in chapter 2.In chapter 4,four numerical examples are carried out to simulate the results,specific data are used for analysis and comparison to verify the rationality and validity of the above conclusions.