Rehabilitation technologies have great potentials in assisted motion training for stroke patients. Considering that wrist motion plays an important role in arm dexterous manipulation of activities of daily living, this paper focuses on developing a c...
IEEE transactions on neural networks and learning systems
May 1, 2015
Diffuse optical tomography (DOT) reconstructs 3-D tomographic images of brain activities from observations by near-infrared spectroscopy (NIRS) that is formulated as an ill-posed inverse problem. This brief presents a method for NIRS DOT based on a h...
IEEE transactions on neural networks and learning systems
May 1, 2015
The use of domain knowledge in learning systems is expected to improve learning efficiency and reduce model complexity. However, due to the incompatibility with knowledge structure of the learning systems and real-time exploratory nature of reinforce...
PURPOSE: The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Int...
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...
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
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...
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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