IEEE transactions on neural networks and learning systems
Dec 25, 2014
In this paper, the stability problem is studied for a class of stochastic neural networks (NNs) with local impulsive effects. The impulsive effects considered can be not only nonidentical in different dimensions of the system state but also various a...
IEEE transactions on neural networks and learning systems
Dec 24, 2014
Kohonen's self-organizing map (SOM) is used to map high-dimensional data into a low-dimensional representation (typically a 2-D or 3-D space) while preserving their topological characteristics. A major reason for its application is to be able to visu...
Environmental science and pollution research international
Dec 20, 2014
The present investigation deals with the prediction of the performance of simultaneous anaerobic sulfide and nitrate removal in an upflow anaerobic sludge bed (UASB) reactor through an artificial neural network (ANN). Influent sulfide concentration, ...
IEEE transactions on neural networks and learning systems
Dec 19, 2014
This paper presents a tracking control methodology for a class of uncertain nonlinear systems subject to input saturation constraint and external disturbances. Unlike most previous approaches on saturated systems, which assumed affine nonlinear syste...
IEEE transactions on neural networks and learning systems
Dec 19, 2014
In this paper, we propose new methods for the choice and adaptation of the smoothing parameter of the probabilistic neural network (PNN). These methods are based on three reinforcement learning algorithms: Q(0)-learning, Q(λ)-learning, and stateless ...
IEEE transactions on neural networks and learning systems
Dec 18, 2014
Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-base...
IEEE transactions on neural networks and learning systems
Dec 18, 2014
Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Spa...
Neural networks : the official journal of the International Neural Network Society
Dec 18, 2014
This paper presents theoretical results on the convergence and attractivity of memristor-based cellular neural networks (MCNNs) with time delays. Based on a realistic memristor model, an MCNN is modeled using a differential inclusion. The essential b...
Neural networks : the official journal of the International Neural Network Society
Dec 18, 2014
Recent efforts to develop large-scale brain and neurocognitive architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason for this is that most conventional SOMs use a static encoding representat...
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