Multistability in bidirectional associative memory neural networks

In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2n  -dimensional networks can have n33n equilibria and n22n equilibria of them are locally exponentially stable, where each layer of the BAM network has n   neurons. Furthermore, the results has been extended to (n+m)(n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results.