Learn More
This brief studies the global exponential stability of the equilibrium point of discrete-time delayed Hopfield neural networks (DHNNs) with impulse effects by using difference inequalities. We shall consider the stabilizing effects of impulses when the corresponding impulse-free DHNN is even not asymptotically stable. The obtained results characterize the(More)
This paper studies the stochastic robust stability for impulsive interval neural networks with distributed time-varying delays of neutral type. The present model is a more general description of the real world in nature, where both discrete delays and distributed delays are taken into consideration. The parameter uncertainties are assumed to be bounded and(More)
Rigorous design of experiment (DOE) is essential to conduct validation, uncertainty quantification (UQ), and sensitivity analysis (SA) of computer simulation models. However, executing the process often involves knowledge of data management, statistical design, running simulation model, data analysis, and so on. It is a non-trivial task even for domain(More)
Network-based computer simulation models are powerful tools for analyzing and guiding policy formation related to the actual systems being modeled. However, the inherent data and computationally intensive nature of this model class gives rise to fundamental challenges when it comes to executing typical experimental designs. In particular this applies to(More)
In this paper we extend the notion of activity for Boolean networks introduced by Shmulevich and Kauffman (Phys Rev Lett 93(4):48701:1–4, 2004). In contrast to existing theory, we take into account the actual graph structure of the Boolean network. The notion of activity measures the probability that a perturbation in an initial state produces a different(More)