Neural networks for computing best rank-one approximations of tensors and its applications

发布日期: 2017-11-07  作者:    浏览次数: 13 

报告题目Neural networks for computing best rank-one    approximations of tensors and its applications


报告人:复旦大学 魏益民教授






报告摘要This talk presents the neural dynamical network to compute a best rank-one approximation of a real- valued tensor. We implement the neural network model by the ordinary differential equations (ODE), which is a class of continuous-time recurrent neural network. Several new properties of solutions for the neural network are established. We prove that the locally asymptotic stability of solutions for ODE by constructive an appropriate Lyapunov function under mild conditions.





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