Analysis and Design of Associative Memories for Memristive Neural Networks with Deviating Argument

Abstract EN

We investigate associative memories for memristive neural networks with deviating argument.

Firstly, the existence and uniqueness of the solution for memristive neural networks with deviating argument are discussed.

Next, some sufficient conditions for this class of neural networks to possess invariant manifolds are obtained.

In addition, a global exponential stability criterion is presented.

Then, analysis and design of autoassociative memories and heteroassociative memories for memristive neural networks with deviating argument are formulated, respectively.

Finally, several numerical examples are given to demonstrate the effectiveness of the obtained results.