Neural networks : the official journal of the International Neural Network Society
Oct 10, 2022
This paper mainly attempts to discuss lag H synchronization in multiple state or derivative coupled reaction-diffusion neural networks without and with parameter uncertainties. Firstly, we respectively propose two types of reaction-diffusion neural n...
Neural networks : the official journal of the International Neural Network Society
Oct 8, 2022
This paper investigates the pinning synchronization of stochastic neutral memristive neural networks with reaction-diffusion terms. Firstly, two novel pinning controllers, which contain both current state and past state, are designed. Subsequently, i...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
This article investigates the stability and synchronization of nonautonomous reaction-diffusion neural networks with general time-varying delays. Compared with the existing works concerning reaction-diffusion neural networks, the main innovation of t...
IEEE transactions on nanobioscience
Sep 26, 2022
The unconventional nature of molecular communication necessitates contributions from a host of scientific fields making the simulator design for such systems to be quite challenging. The nervous system is one of the largest and most important nanonet...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
For the considered reaction-diffusion inertial memristive neural networks (IMNNs), this article proposes a novel gain-scheduled generalized pinning control scheme, where three pinning control strategies are involved and 2 controller gains can be sche...
ISA transactions
Aug 20, 2022
The finite-time synchronization issue of reaction-diffusion memristive neural networks (RDMNNs) is studied in this paper. To better synchronize the parameter-varying drive and response systems, an innovative gain-scheduled integral sliding mode contr...
Journal of computational chemistry
Aug 9, 2022
Molecular self-diffusion coefficients underlie various kinetic properties of the liquids involved in chemistry, physics, and pharmaceutics. In this study, 547 self-diffusion coefficients are calculated based on all-atom molecular dynamics (MD) simula...
IEEE transactions on neural networks and learning systems
Aug 3, 2022
Link prediction (LP) in networks aims at determining future interactions among elements; it is a critical machine-learning tool in different domains, ranging from genomics to social networks to marketing, especially in e-commerce recommender systems....
IEEE transactions on neural networks and learning systems
Aug 3, 2022
This study considers the boundary stabilization for stochastic delayed Cohen-Grossberg neural networks (SDCGNNs) with diffusion terms by the Lyapunov functional method. In the realization of NNs, sometimes time delays and diffusion phenomenon cannot ...
IEEE transactions on cybernetics
Jul 4, 2022
In this article, we consider the problem of distributed adaptive leader-follower coordination of partial differential systems (i.e., reaction-diffusion neural networks, RDNNs) with directed communication topology in the case of multiple leaders. Diff...