It is a well-known fact that oscillatory networks can operate as Hoppeld-like neural networks, the only difference being that their attractors are limit cycles: one for each memorized pattern. The neuron activities are synchronized on the limit cycles, and neurons oscillate with xed phase diierences (time delays). We prove that this property is a natural attribute of general weakly connected ne...

This paper is concerned with pth moment exponential stability of stochastic reaction-diffusion Cohen-Grossberg neural networks with time-varying delays. With the help of Lyapunov method, stochastic analysis, and inequality techniques, a set of new suffcient conditions on pth moment exponential stability for the considered system is presented. The proposed results generalized and improved some e...

In this paper, a more general class of state-dependent impulsive Cohen–Grossberg neural networks having variable coefficients with time-varying delays is addressed. By means of B-equivalence method, we reduce this state-dependent impulsive neural networks system to a fix time impulsive neural networks system. Sufficient conditions for existence and global exponential stability of the equilibriu...

Abstract. In this paper, by using a continuation theorem of coincidence degree theory and a differential inequality, we establish some sufficient conditions ensuring the existence and global exponential stability of anti-periodic solutions for a class of fuzzy Cohen–Grossberg neural networks with time-varying and distributed delays. In addition, we present an illustrative example to show the fe...

A class of Cohen-Grossberg neural networks with distributed delays are considered. By using the coincidence degree theorem and differential inequality techniques, sufficient conditions for the existence and exponential stability of the periodic solutions are established, Without assuming the boundedness, monotonicity, and differentiability of activation functions and any symmetry of interconnec...