A class of delayed BAM self-adaptive neural network with uncertain parameters for projective synchronization problem

This paper discusses a class of delayed Bidirectional Associate Memory (BAM for short) neural network in projective synchronization under uncertain parameters problem. Basing on BAM classic models and the Lyapunov stability theory, the thesis designs a new self-adaptive control method pointing at the characteristics of neural network. This method which other articles rarely mention can be realized in BAM system parameter identification and implementation of multidimensional projective synchronization of master-slave system. Through the numerical simulation, feasibility of this method has been justified.