IEEE transactions on neural networks and learning systems
Apr 1, 2015
This paper investigates the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs). Two types of DDNNs with stabilizing impulses are studied. By introducing the time-varying Lyapunov functio...
An in silico method for predicting percutaneous absorption of cosmetic ingredients was developed by using artificial neural network (ANN) analysis to predict the human skin permeability coefficient (log Kp), taking account of the physicochemical prop...
Networks are well suited to display and analyze complex systems that consist of numerous and interlinked elements. This study aimed at: (1) generating a series of drug prescription networks (DPNs) displaying co-prescription in community-dwelling elde...
PURPOSE: A unique capability of the CyberKnife system is dynamic target tracking. However, not all patients are eligible for this approach. Rather, their tumors are tracked statically using the vertebral column for alignment. When using static tracki...
IEEE transactions on neural networks and learning systems
Mar 1, 2015
Successful biological systems adapt to change. In this paper, we are principally concerned with adaptive systems that operate in environments where data arrives sequentially and is multivariate in nature, for example, sensory streams in robotic syste...
IEEE transactions on neural networks and learning systems
Mar 1, 2015
In this paper, the finite-horizon optimal control design for nonlinear discrete-time systems in affine form is presented. In contrast with the traditional approximate dynamic programming methodology, which requires at least partial knowledge of the s...
IEEE transactions on neural networks and learning systems
Mar 1, 2015
The stochastic optimal control of nonlinear networked control systems (NNCSs) using neuro-dynamic programming (NDP) over a finite time horizon is a challenging problem due to terminal constraints, system uncertainties, and unknown network imperfectio...
IEEE transactions on neural networks and learning systems
Feb 1, 2015
Learning algorithms play an important role in the practical application of neural networks based on principal component analysis, often determining the success, or otherwise, of these applications. These algorithms cannot be divergent, but it is very...
BACKGROUND: Minimally invasive approaches to kidney transplantation (KT) have been described recently. However, information concerning perioperative management in these patients is lacking. Accordingly, in the current study, we describe our periopera...
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