Bifurcation and Controller Design of 5D BAM Neural Networks With Time Delay

Abstract

All the time delayed dynamical system plays a vital role in describing the dynamical phenomenon of neural networks. In the current article, we study a class of 5D delayed bidirectional associative memory (BAM) neural networks that conform to objective reality. First of all, we prove that the solution of the delayed 5D BAM neural networks exists and is unique by virtue of fixed point theorem and some inequality techniques. Secondly, the Hopf bifurcation and stability of the delayed 5D BAM neural networks are investigated by exploiting the stability criterion and bifurcation theory. Once more, Hopf bifurcation control strategy of the delayed 5D BAM neural networks is explored by virtue of two different hybrid controllers. By adjusting the parameters of the controllers, we can control the stability domain and Hopf bifurcation onset. Eventually, the correctness of the theoretical results was verified through numerical simulations. The conclusions obtained in this paper are new and have important theoretical value in neural network area.