Chaotic and hyperchaotic attractors in time-delayed neural networks

Abstract

It is well known that complex dynamic behaviors exist in time-delayed neural networks. Infinite positive Lyapunov exponents can be found in time-delayed chaotic systems since the dimension of such systems is infinite. This paper presents an infinite-dimension hyperchaotic time-delayed neuron system with sinusoidal activation function. The hyperchaotic neuron system is studied by Lyapunov exponent, phase diagram, Poincare section and power spectrum. Numerical simulations show that the new system's behavior can be convergent, periodic, chaotic and hyperchaotic when the time-delay parameter varies. © 2009 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.