IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 15, 2014
In human locomotion, we continuously modulate joint mechanical impedance of the lower limb (hip, knee, and ankle) either voluntarily or reflexively to accommodate environmental changes and maintain stable interaction. Ankle mechanical impedance plays...
BACKGROUND: The implementation of robotic technology in minimally invasive surgery has led to the need to develop more efficient and effective training methods, as well as assessment and skill maintenance tools for surgical education. Multiple simula...
IEEE transactions on neural networks and learning systems
Aug 6, 2014
This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by ne...
In this paper, a novel approach to fuzzy sampled-data control of chaotic systems is presented by using a time-dependent Lyapunov functional. The advantage of the new method is that the Lyapunov functional is continuous at sampling times but not neces...
This paper proposes a novel model-based tracking approach for 3-D localization. One main difficulty of standard model-based approach lies in the presence of low-level ambiguities between different edges. In this paper, given a 3-D model of the edges ...
IEEE transactions on neural networks and learning systems
Jul 23, 2014
This brief considers the guaranteed H∞ performance state estimation problem of delayed static neural networks. An Arcak-type state estimator, which is more general than the widely adopted Luenberger-type one, is chosen to tackle this issue. A delay-d...
IEEE transactions on neural networks and learning systems
Jul 23, 2014
An extreme learning machine (ELM) can be regarded as a two-stage feed-forward neural network (FNN) learning system that randomly assigns the connections with and within hidden neurons in the first stage and tunes the connections with output neurons i...
IEEE transactions on neural networks and learning systems
Jul 22, 2014
Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order stri...
IEEE transactions on neural networks and learning systems
Jul 21, 2014
This paper presents an adaptive output-feedback neural network (NN) control scheme for a class of stochastic nonlinear time-varying delay systems with unknown control directions. To make the controller design feasible, the unknown control coefficient...
IEEE transactions on neural networks and learning systems
Jul 21, 2014
An extreme learning machine (ELM) is a feedforward neural network (FNN) like learning system whose connections with output neurons are adjustable, while the connections with and within hidden neurons are randomly fixed. Numerous applications have dem...
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