Quantum mechanics

Control (management)

Multistability

Applied mathematics

Stability (learning theory)

Computer science

Integer (computer science)

Physics

Nonlinear system

Instability

Pattern recognition (psychology)

Mathematics

Artificial intelligence

Programming language

Control theory (sociology)

Artificial neural network

Mechanics

Recurrent neural network

Machine learning

Neural Networks

This paper provides new theoretical results on the multistability and instability analysis of recurrent neural networks with time-varying delays. It is shown that such n-neuronal recurrent neural networks have exactly [Formula: see text] equilibria, [Formula: see text] of which are locally exponentially stable and the others are unstable, where k0 is a nonnegative integer such that k0≤n. By using the combination method of two different divisions, recurrent neural networks can possess more dynamic properties. This method improves and extends the existing results in the literature. Finally, one numerical example is provided to show the superiority and effectiveness of the presented results.

Multistability and instability analysis of recurrent neural networks with time-varying delays