Sentence examples for recurrent diffusion from inspiring English sources
Let X t) be a positive recurrent diffusion process corresponding to an operator L on a domain D⊆Rd with oblique reflection at ∂D if D≠Rd.
Model (2.1) includes the following reaction-diffusion recurrent neural networks (RNNs) as a special case: (2.2). for.
In [36], the problem of stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters have been investigated.
Recently, Sun et al. [32, 33] have studied the problem of the almost sure exponential stability and the moment exponential stability of an equilibrium solution for stochastic reaction-diffusion recurrent neural networks with continuously distributed delays and constant delays, respectively.
To the best of our knowledge, few authors have considered the synchronization of reaction-diffusion recurrent fuzzy neural networks with delays and Dirichlet boundary conditions on time scales which is a challenging and important problem in theory and application.
In Sections 3 and 4, criteria are proposed to determine global exponential stability, and periodic solutions are considered for reaction-diffusion recurrent neural networks with mixed time delays, respectively.
Recurrent tumours show restricted diffusion, whereas post-treatment changes have high signal intensity on apparent diffusion coefficient (ADC) maps.
In this case, system (1.1 - 1.3 1.1 - 1.3ontisuous reacthen-diffusion BAM recontinuousNs (1.4)-(1.6).
If T = Z, then μ ( t ) = 1, system (1.1 - 1.3 1.1 - 1.3iscrethediscretece reaction-differenceBAM recurrent FNNs (1.7)-(1.9).
In this paper, we study the global robust exponential synchronization of reaction-diffusion BAM recurrent FNNs (1.1 - 1.3 1.1 - 1.3nify both the continuous case and the discrete difference case.
In this article, the global robust exponential synchronization of reaction-diffusion BAM recurrent fuzzy neural networks (FNNs) with infinite distributed delays on time scales is investigated.
