AIMC Journal:
IEEE transactions on neural networks and learning systems
Showing 801 to 810 of 817 articles
Impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks.
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...
Adaptive optimal control of highly dissipative nonlinear spatially distributed processes with neuro-dynamic programming.
IEEE transactions on neural networks and learning systems
Apr 1, 2015
Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly ...
Quaternion-valued echo state networks.
IEEE transactions on neural networks and learning systems
Apr 1, 2015
Quaternion-valued echo state networks (QESNs) are introduced to cater for 3-D and 4-D processes, such as those observed in the context of renewable energy (3-D wind modeling) and human centered computing (3-D inertial body sensors). The introduction ...
Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.
IEEE transactions on neural networks and learning systems
Mar 1, 2015
This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the un...
A simplified adaptive neural network prescribed performance controller for uncertain MIMO feedback linearizable systems.
IEEE transactions on neural networks and learning systems
Mar 1, 2015
In this paper, the problem of deriving a continuous, state-feedback controller for a class of multiinput multioutput feedback linearizable systems is considered with special emphasis on controller simplification and reduction of the overall design co...
Spatio-temporal learning with the online finite and infinite echo-state Gaussian processes.
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...
Neural network-based finite-horizon optimal control of uncertain affine nonlinear discrete-time systems.
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...
Neural network-based finite horizon stochastic optimal control design for nonlinear networked control systems.
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...
Scaling up graph-based semisupervised learning via prototype vector machines.
IEEE transactions on neural networks and learning systems
Mar 1, 2015
When the amount of labeled data are limited, semisupervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the un...
An enhanced fuzzy min-max neural network for pattern classification.
IEEE transactions on neural networks and learning systems
Mar 1, 2015
An enhanced fuzzy min-max (EFMM) network is proposed for pattern classification in this paper. The aim is to overcome a number of limitations of the original fuzzy min-max (FMM) network and improve its classification performance. The key contribution...
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