Research On Synchronization Of Neural Networks Based On Impulsive Control

The actual infrastructure presents complex,changeable,nonlinear and other characteristics in the operation process,and the general mathematical method is difficult to model it.In order to meet the needs of the society,this dissertation makes more in-depth theoretical research on the actual infrastructure system.Using some basic principles and functions of neural networks(NNs)and fuzzy neural networks(FNNs),such as the powerful nonlinear learning function of neural networks,and the unique processing function of fuzzy neural networks for complex and changeable information,to establish a discrete-time dynamic model for infrastructure systems.Consider the problems of uncertain information interaction,actuator failure,incomplete system state,etc.Designing the impulse signal estimator and controller to ensure the stability of extension error system and achieve the synchronization of neural networks.The contents of the study are as follows:Firstly,this dissertation investigates synchronization for a group of discrete time NNs with the uncertain exchanging information,which is caused by the uncertain connection weights among the NNs nodes,and they are transformed into a norm bounded uncertain Laplacian matrix.The distributed impulsive observers,which possess the advantage of reducing the communication load among NNs nodes,are designed to observe the NNs state.The impulsive controller is proposed to improve the efficiency of the controller.An impulsive augmented error system(IAES)is obtained based on the matrix kronecker product.A sufficient condition is established to ensure synchronization of the group of NNs by proving the stability of the IAES.An iterative algorithm is given to obtain a suboptimal allowed interval of the impulsive signal,and the corresponding gains of the observer and the controller are derived.The developed result is illustrated by a numerical example.Secondly,this dissertation studies the synchronization of the master-slave(MS)fuzzy neural networks with random actuator failure,where only partial state information of the master FNNs is available.To reduce the loads of the communication channel and the controller,the simultaneously impulsive driven strategy of the communication chann el and the controller is proposed.On the basis of the received measurements of the master FNNs,the mixed controller consisting of observer based controller and the static one is designed.The randomly occurred actuator failure is also considered.Accord ing to the Lyapunov method,the sufficient conditions are achieved to ensure the synchronization of the MS FNNs,and the controller gains are designed using the obtained results.The validity of the derived results is illustrated by a numerical example.Finally,some theoretical research results are applied to the practical system,the results and innovations of this dissertation are summarized,and the future research problems are planned.