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Early rehabilitation improves neurofunctional outcome after surgery in children with spinal tumors

Neural Regeneration Research 2014年第2期9卷 129-134页

作者： Nezire Kose Ozge Muezzinoglu Sevil Bilgin Sevilay Karahan Ilkay Is kay Burcak Bilginer Department of Physiotherapy and Rehabilitation Faculty of Health SciencesHacettepe University Department of Biostatistics Faculty of Health SciencesHacettepe University Department of Neurosurgery Faculty of MedicineHacettepe University

To investigate the effect of early rehabilitation on neurofunctional outcome after surgery in chil- dren with spinal tumors, this study reviewed the medical charts and radiographic records of 70 pediatric patients （1-17 years old） who received spinal tumor surgical removal. The peddiatric patients received rahabilitation treatment at 4 （range, 2-7） days after surgery for 10 （range, 7-23） days. Results from the Modified McCormick Scale, functional Independence Measure for Chil- dren, American Spinal Injury Association Impairment Scale and Karnofsky Performance Status Scale demonstrated that the sensory function, motor function and activity of daily living of pedi- atric children who received early rehabilitation were significantly improved. Results also showed that tumor setting and level localization as well as patients＇s clinical symptoms have no influences on neurofunctional outcomes.

关键词： nerve regeneration spinal cord injury spinal cord tumor children rehabilitation func-tion sense motor retrospective analysis neural regeneration

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Some Features of Neural Networks as Nonlinearly Parameterized Models of Unknown Systems Using an Online Learning Algorithm

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Journal of Applied Mathematics and Physics 2018年第1期6卷 247-263页

作者： Leonid S. Zhiteckii Valerii N. Azarskov Sergey A. Nikolaienko Klaudia Yu. Solovchuk Department of Intelligent Automatic Systems International Centre of Information Technologies and Systems Institute of Cybernetics Kiev Ukraine Aircraft Control Systems Department National Aviation University Kiev Ukraine

This paper deals with deriving the properties of updated neural network model that is exploited to identify an unknown nonlinear system via the standard gradient learning algorithm. The convergence of this algorithm for online training the three-layer neural networks in stochastic environment is studied. A special case where an unknown nonlinearity can exactly be approximated by some neural network with a nonlinear activation function for its output layer is considered. To analyze the asymptotic behavior of the learning processes, the so-called Lyapunov-like approach is utilized. As the Lyapunov function, the expected value of the square of approximation error depending on network parameters is chosen. Within this approach, sufficient conditions guaranteeing the convergence of learning algorithm with probability 1 are derived. Simulation results are presented to support the theoretical analysis.

关键词： Neural Network Nonlinear Model Online Learning Algorithm Lyapunov func-tion Probabilistic Convergence

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